{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T10:39:23Z","timestamp":1785494363242,"version":"3.56.0"},"reference-count":566,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS-2024-00450102"],"award-info":[{"award-number":["RS-2024-00450102"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Chemical Engineering"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1016\/j.compchemeng.2025.109266","type":"journal-article","created":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T06:29:13Z","timestamp":1753511353000},"page":"109266","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":12,"special_numbering":"C","title":["Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective"],"prefix":"10.1016","volume":"203","author":[{"given":"Youhyun","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hayoung","family":"Doo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daeun","family":"Shin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seo Yoon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yugyeong","family":"Roh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seongeun","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heejin","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujin","family":"Jung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyuk Jun","family":"Yoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang Soo","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong Woo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian O.","family":"Besenhard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye Seol","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-1106-9500","authenticated-orcid":false,"given":"Jonggeol","family":"Na","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b1","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202170022","article-title":"Self-driven multistep quantum dot synthesis enabled by autonomous robotic experimentation in flow","volume":"3","author":"Abdel-Latif","year":"2021","journal-title":"Adv. Intell. Syst."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b2","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1038\/s44160-022-00231-0","article-title":"The rise of self-driving labs in chemical and materials sciences","volume":"2","author":"Abolhasani","year":"2023","journal-title":"Nat. Synth."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b3","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.matt.2024.01.005","article-title":"Human-in-the-loop for Bayesian autonomous materials phase mapping","volume":"7","author":"Adams","year":"2024","journal-title":"Matter"},{"key":"10.1016\/j.compchemeng.2025.109266_b4","series-title":"Computer Aided Chemical Engineering","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/B978-0-444-63433-7.50007-9","article-title":"Molecules matter: the expanding envelope of process design","volume":"vol. 34","author":"Adjiman","year":"2014"},{"issue":"14","key":"10.1016\/j.compchemeng.2025.109266_b5","doi-asserted-by":"crossref","first-page":"5194","DOI":"10.1021\/acs.iecr.0c05399","article-title":"Process systems engineering perspective on the design of materials and molecules","volume":"60","author":"Adjiman","year":"2021","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b6","series-title":"Lab automation liquid handling, automated liquid handling \u2014 Agilent \u2014 agilent.com","author":"Agilent","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2022.139707","article-title":"Exploring ultrafast flow chemistry by autonomous self-optimizing platform","volume":"453","author":"Ahn","year":"2023","journal-title":"Chem. Eng. J."},{"key":"10.1016\/j.compchemeng.2025.109266_b8","series-title":"Extracting structured data from organic synthesis procedures using a fine-tuned large language model","author":"Ai","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b9","series-title":"Schedule optimization for chemical library synthesis","author":"Ai","year":"2024"},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b10","doi-asserted-by":"crossref","first-page":"3222","DOI":"10.1109\/TASE.2021.3114157","article-title":"A robust asymmetric kernel function for Bayesian optimization, with application to image defect detection in manufacturing systems","volume":"19","author":"AlBahar","year":"2021","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b11","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1038\/s42004-024-01341-w","article-title":"Leveraging infrared spectroscopy for automated structure elucidation","volume":"7","author":"Alberts","year":"2024","journal-title":"Commun. Chem."},{"key":"10.1016\/j.compchemeng.2025.109266_b12","series-title":"QSPR\/QSAR Analysis using SMILES and Quasi-SMILES","first-page":"85","article-title":"All SMILES variational autoencoder for molecular property prediction and optimization","author":"Alperstein","year":"2023"},{"key":"10.1016\/j.compchemeng.2025.109266_b13","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s10479-015-2019-x","article-title":"Simulation optimization: a review of algorithms and applications","volume":"240","author":"Amaran","year":"2016","journal-title":"Ann. Oper. Res."},{"issue":"8029","key":"10.1016\/j.compchemeng.2025.109266_b14","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1038\/s41586-024-07892-1","article-title":"Closed-loop transfer enables artificial intelligence to yield chemical knowledge","volume":"633","author":"Angello","year":"2024","journal-title":"Nature"},{"issue":"16","key":"10.1016\/j.compchemeng.2025.109266_b15","doi-asserted-by":"crossref","first-page":"8736","DOI":"10.1021\/jacs.2c13467","article-title":"Generative models as an emerging paradigm in the chemical sciences","volume":"145","author":"Anstine","year":"2023","journal-title":"J. Am. Chem. Soc."},{"key":"10.1016\/j.compchemeng.2025.109266_b16","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b17","doi-asserted-by":"crossref","DOI":"10.1002\/syst.202400006","article-title":"Evidence of selection in mineral mediated polymerization reactions executed in a robotic chemputer system","volume":"6","author":"Asche","year":"2024","journal-title":"ChemSystemsChem"},{"issue":"11","key":"10.1016\/j.compchemeng.2025.109266_b18","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1109\/JPROC.2020.3026619","article-title":"Advances in asynchronous parallel and distributed optimization","volume":"108","author":"Assran","year":"2020","journal-title":"Proc. IEEE"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b19","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1137\/040603371","article-title":"Mesh adaptive direct search algorithms for constrained optimization","volume":"17","author":"Audet","year":"2006","journal-title":"SIAM J. Optim."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b20","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1137\/18M1175872","article-title":"The mesh adaptive direct search algorithm for granular and discrete variables","volume":"29","author":"Audet","year":"2019","journal-title":"SIAM J. Optim."},{"key":"10.1016\/j.compchemeng.2025.109266_b21","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.ces.2016.05.025","article-title":"A COSMO-based approach to computer-aided mixture design","volume":"159","author":"Austin","year":"2017","journal-title":"Chem. Eng. Sci."},{"issue":"15","key":"10.1016\/j.compchemeng.2025.109266_b22","doi-asserted-by":"crossref","first-page":"4401","DOI":"10.1021\/acs.jpclett.9b01428","article-title":"Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts","volume":"10","author":"Back","year":"2019","journal-title":"J. Phys. Chem. Lett."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b23","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1038\/s41467-023-44599-9","article-title":"A dynamic knowledge graph approach to distributed self-driving laboratories","volume":"15","author":"Bai","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compchemeng.2025.109266_b24","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.compchemeng.2019.05.008","article-title":"UNIPOPT: Univariate projection-based optimization without derivatives","volume":"127","author":"Bajaj","year":"2019","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b25","first-page":"21524","article-title":"BoTorch: A framework for efficient Monte\u2013Carlo Bayesian optimization","volume":"33","author":"Balandat","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b26","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.1039\/D2RA07812K","article-title":"Deep learning for automated size and shape analysis of nanoparticles in scanning electron microscopy","volume":"13","author":"Bals","year":"2023","journal-title":"RSC Adv."},{"key":"10.1016\/j.compchemeng.2025.109266_b27","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10107-012-0567-2","article-title":"Tractable stochastic analysis in high dimensions via robust optimization","volume":"134","author":"Bandi","year":"2012","journal-title":"Math. Program."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b28","doi-asserted-by":"crossref","first-page":"2834","DOI":"10.1021\/ie901281w","article-title":"Continuous-molecular targeting for integrated solvent and process design","volume":"49","author":"Bardow","year":"2010","journal-title":"Ind. Eng. Chem. Res."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b29","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/s43586-022-00141-7","article-title":"Serial femtosecond crystallography","volume":"2","author":"Barends","year":"2022","journal-title":"Nat. Rev. Methods Prim."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b30","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1177\/2472630319877374","article-title":"FINDUS: An open-source 3D printable liquid-handling workstation for laboratory automation in life sciences","volume":"25","author":"Barthels","year":"2020","journal-title":"SLAS Technol.: Transl. Life Sci. Innov."},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b31","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202270020","article-title":"Autonomous nanocrystal doping by self-driving fluidic micro-processors","volume":"4","author":"Bateni","year":"2022","journal-title":"Adv. Intell. Syst."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b32","article-title":"Smart dope: a self-driving fluidic lab for accelerated development of doped perovskite quantum dots","volume":"14","author":"Bateni","year":"2024","journal-title":"Adv. Energy Mater."},{"key":"10.1016\/j.compchemeng.2025.109266_b33","series-title":"The Architecture of Open Source Applications Volume II: Structure, Scale, and a Few more Fearless Hacks","article-title":"Sqlalchemy","author":"Bayer","year":"2012"},{"issue":"7","key":"10.1016\/j.compchemeng.2025.109266_b34","doi-asserted-by":"crossref","first-page":"2382","DOI":"10.1016\/j.matt.2024.06.003","article-title":"Autonomous chemistry: Navigating self-driving labs in chemical and material sciences","volume":"7","author":"Bayley","year":"2024","journal-title":"Matter"},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b35","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1021\/acs.chemmater.2c03118","article-title":"The autonomous formulation laboratory: An open liquid handling platform for formulation discovery using x-ray and neutron scattering","volume":"35","author":"Beaucage","year":"2023","journal-title":"Chem. Mater."},{"issue":"6408","key":"10.1016\/j.compchemeng.2025.109266_b36","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1126\/science.aat0650","article-title":"Reconfigurable system for automated optimization of diverse chemical reactions","volume":"361","author":"B\u00e9dard","year":"2018","journal-title":"Science"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b37","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1038\/s44286-023-00024-y","article-title":"Autonomous execution of highly reactive chemical transformations in the schlenkputer","volume":"1","author":"Bell","year":"2024","journal-title":"Nat. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b38","first-page":"1","article-title":"Robotic synthesis decoded through phase diagram mastery","author":"Bennett","year":"2024","journal-title":"Nat. Synth."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b39","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1038\/s44286-024-00033-5","article-title":"Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory","volume":"1","author":"Bennett","year":"2024","journal-title":"Nat. Chem. Eng."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b40","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b41","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1021\/cg501637m","article-title":"Crystal size control in a continuous tubular crystallizer","volume":"15","author":"Besenhard","year":"2015","journal-title":"Cryst. Growth Des."},{"issue":"12","key":"10.1016\/j.compchemeng.2025.109266_b42","doi-asserted-by":"crossref","first-page":"6432","DOI":"10.1021\/acs.cgd.7b01096","article-title":"Crystal engineering in continuous plug-flow crystallizers","volume":"17","author":"Besenhard","year":"2017","journal-title":"Cryst. Growth Des."},{"issue":"8","key":"10.1016\/j.compchemeng.2025.109266_b43","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.1007\/s11837-016-2001-3","article-title":"The materials data facility: data services to advance materials science research","volume":"68","author":"Blaiszik","year":"2016","journal-title":"Jom"},{"issue":"32","key":"10.1016\/j.compchemeng.2025.109266_b44","doi-asserted-by":"crossref","first-page":"14590","DOI":"10.1021\/jacs.2c03631","article-title":"Fully automated unconstrained analysis of high-resolution mass spectrometry data with machine learning","volume":"144","author":"Boiko","year":"2022","journal-title":"J. Am. Chem. Soc."},{"issue":"7992","key":"10.1016\/j.compchemeng.2025.109266_b45","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1038\/s41586-023-06792-0","article-title":"Autonomous chemical research with large language models","volume":"624","author":"Boiko","year":"2023","journal-title":"Nature"},{"issue":"11","key":"10.1016\/j.compchemeng.2025.109266_b46","doi-asserted-by":"crossref","first-page":"3232","DOI":"10.3390\/pr11113232","article-title":"Adaptive latin hypercube sampling for a surrogate-based optimization with artificial neural network","volume":"11","author":"Borisut","year":"2023","journal-title":"Processes"},{"issue":"6092","key":"10.1016\/j.compchemeng.2025.109266_b47","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1126\/science.1217737","article-title":"High-resolution protein structure determination by serial femtosecond crystallography","volume":"337","author":"Boutet","year":"2012","journal-title":"Science"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b48","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1039\/C9ME00089E","article-title":"Beyond a heuristic analysis: integration of process and working-fluid design for organic rankine cycles","volume":"5","author":"Bowskill","year":"2020","journal-title":"Mol. Syst. Des. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b49","series-title":"Applied Cryptography and Network Security: Second International Conference, ACNS 2004, Yellow Mountain, China, June 8-11, 2004. Proceedings 2","first-page":"292","article-title":"SQLrand: Preventing SQL injection attacks","author":"Boyd","year":"2004"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b50","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s10898-018-0609-2","article-title":"Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm","volume":"71","author":"Bradford","year":"2018","journal-title":"J. Global Optim."},{"key":"10.1016\/j.compchemeng.2025.109266_b51","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/0378-3812(86)85016-6","article-title":"A strategy for the design and selection of solvents for separation processes","volume":"29","author":"Brignole","year":"1986","journal-title":"Fluid Phase Equilib."},{"issue":"7815","key":"10.1016\/j.compchemeng.2025.109266_b52","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1038\/s41586-020-2442-2","article-title":"A mobile robotic chemist","volume":"583","author":"Burger","year":"2020","journal-title":"Nature"},{"issue":"10","key":"10.1016\/j.compchemeng.2025.109266_b53","doi-asserted-by":"crossref","first-page":"3249","DOI":"10.1002\/aic.14838","article-title":"A hierarchical method to integrated solvent and process design of physical CO 2 absorption using the SAFT-\u03b3 Mie approach","volume":"61","author":"Burger","year":"2015","journal-title":"AIChE J."},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b54","doi-asserted-by":"crossref","first-page":"2667","DOI":"10.1021\/acs.jcim.2c01569","article-title":"Mf-pcba: Multifidelity high-throughput screening benchmarks for drug discovery and machine learning","volume":"63","author":"Buterez","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compchemeng.2025.109266_b55","series-title":"Autonomous Experimentation for Molecular Discovery Applications","author":"Canty","year":"2024"},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b56","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1039\/D3DD00135K","article-title":"Integrating autonomy into automated research platforms","volume":"2","author":"Canty","year":"2023","journal-title":"Digit. Discov."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b57","article-title":"Optimization of formulations using robotic experiments driven by machine learning DoE","volume":"2","author":"Cao","year":"2021","journal-title":"Cell Rep. Phys. Sci."},{"issue":"16","key":"10.1016\/j.compchemeng.2025.109266_b58","doi-asserted-by":"crossref","first-page":"4230","DOI":"10.1039\/D3SC00992K","article-title":"A field guide to flow chemistry for synthetic organic chemists","volume":"14","author":"Capaldo","year":"2023","journal-title":"Chem. Sci."},{"issue":"11","key":"10.1016\/j.compchemeng.2025.109266_b59","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1021\/acscentsci.1c00435","article-title":"Discovering new chemistry with an autonomous robotic platform driven by a reactivity-seeking neural network","volume":"7","author":"Caramelli","year":"2021","journal-title":"ACS Cent. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b60","unstructured":"Castellanos, S., Carnegie Mellon\u2019s cloud lab to automate labor-intensive science experiments."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b61","doi-asserted-by":"crossref","first-page":"9040","DOI":"10.1038\/s41598-020-64397-3","article-title":"Efficient closed-loop maximization of carbon nanotube growth rate using Bayesian optimization","volume":"10","author":"Chang","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compchemeng.2025.109266_b62","series-title":"IBM RXN","author":"for Chemistry","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b63","series-title":"Automated parallel synthesis of organics\u2014Chemspeed","author":"Chemspeed","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b64","series-title":"Automated solid dispensing begins here\u2014Chemspeed","author":"Chemspeed","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b65","first-page":"1","article-title":"Navigating phase diagram complexity to guide robotic inorganic materials synthesis","author":"Chen","year":"2024","journal-title":"Nat. Synth."},{"key":"10.1016\/j.compchemeng.2025.109266_b66","series-title":"A new knowledge gradient-based method for constrained Bayesian optimization","author":"Chen","year":"2021"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b67","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1038\/s41427-022-00416-1","article-title":"Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning","volume":"14","author":"Chen","year":"2022","journal-title":"NPG Asia Mater."},{"issue":"12","key":"10.1016\/j.compchemeng.2025.109266_b68","doi-asserted-by":"crossref","first-page":"8098","DOI":"10.1021\/jacs.3c11852","article-title":"Crystal structure assignment for unknown compounds from X-ray diffraction patterns with deep learning","volume":"146","author":"Chen","year":"2024","journal-title":"J. Am. Chem. Soc."},{"key":"10.1016\/j.compchemeng.2025.109266_b69","series-title":"Fault Detection and Diagnosis in Industrial Systems","author":"Chiang","year":"2000"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b70","article-title":"Introduction to machine learning, neural networks, and deep learning","volume":"9","author":"Choi","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b71","doi-asserted-by":"crossref","first-page":"512","DOI":"10.3390\/en14020512","article-title":"Comparison of factorial and latin hypercube sampling designs for meta-models of building heating and cooling loads","volume":"14","author":"Choi","year":"2021","journal-title":"Energies"},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b72","doi-asserted-by":"crossref","DOI":"10.15252\/msb.20209942","article-title":"Enabling high-throughput biology with flexible open-source automation","volume":"17","author":"Chory","year":"2021","journal-title":"Mol. Syst. Biol."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b73","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1038\/s42004-021-00550-x","article-title":"Data-science driven autonomous process optimization","volume":"4","author":"Christensen","year":"2021","journal-title":"Commun. Chem."},{"issue":"47","key":"10.1016\/j.compchemeng.2025.109266_b74","doi-asserted-by":"crossref","first-page":"15473","DOI":"10.1039\/D1SC04588A","article-title":"Automation isn\u2019t automatic","volume":"12","author":"Christensen","year":"2021","journal-title":"Chem. Sci."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b75","doi-asserted-by":"crossref","DOI":"10.1002\/ange.202214511","article-title":"Bayesian self-optimization for telescoped continuous flow synthesis","volume":"135","author":"Clayton","year":"2023","journal-title":"Angew. Chem."},{"key":"10.1016\/j.compchemeng.2025.109266_b76","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2019.123340","article-title":"Automated self-optimisation of multi-step reaction and separation processes using machine learning","volume":"384","author":"Clayton","year":"2020","journal-title":"Chem. Eng. J."},{"key":"10.1016\/j.compchemeng.2025.109266_b77","series-title":"connorcoley\/ASKCOS: First public release of ASKCOS","author":"Coley","year":"2019"},{"issue":"52","key":"10.1016\/j.compchemeng.2025.109266_b78","doi-asserted-by":"crossref","first-page":"23414","DOI":"10.1002\/anie.201909989","article-title":"Autonomous discovery in the chemical sciences part II: outlook","volume":"59","author":"Coley","year":"2020","journal-title":"Angew. Chem. Int. Ed."},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b79","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1021\/acs.accounts.8b00087","article-title":"Machine learning in computer-aided synthesis planning","volume":"51","author":"Coley","year":"2018","journal-title":"Acc. Chem. Res."},{"issue":"6453","key":"10.1016\/j.compchemeng.2025.109266_b80","doi-asserted-by":"crossref","DOI":"10.1126\/science.aax1566","article-title":"A robotic platform for flow synthesis of organic compounds informed by AI planning","volume":"365","author":"Coley","year":"2019","journal-title":"Science"},{"key":"10.1016\/j.compchemeng.2025.109266_b81","series-title":"Trust Region Methods","author":"Conn","year":"2000"},{"key":"10.1016\/j.compchemeng.2025.109266_b82","series-title":"Cyber Security on Azure: An IT Professional\u2019s Guide To Microsoft Azure Security","first-page":"153","article-title":"Azure security center and azure sentinel","author":"Copeland","year":"2021"},{"issue":"23","key":"10.1016\/j.compchemeng.2025.109266_b83","doi-asserted-by":"crossref","first-page":"14286","DOI":"10.1021\/acs.joc.8b01821","article-title":"An autonomous self-optimizing flow reactor for the synthesis of natural product carpanone","volume":"83","author":"Cort\u00e9s-Borda","year":"2018","journal-title":"J. Org. Chem."},{"key":"10.1016\/j.compchemeng.2025.109266_b84","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/BF00889887","article-title":"The origins of kriging","volume":"22","author":"Cressie","year":"1990","journal-title":"Math. Geol."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b85","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1002\/bit.26192","article-title":"Online optimal experimental re-design in robotic parallel fed-batch cultivation facilities","volume":"114","author":"Cruz Bournazou","year":"2017","journal-title":"Biotechnol. Bioeng."},{"issue":"12","key":"10.1016\/j.compchemeng.2025.109266_b86","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1016\/j.tsf.2010.01.018","article-title":"Synthesis and catalytic properties of metal nanoparticles: Size, shape, support, composition, and oxidation state effects","volume":"518","author":"Cuenya","year":"2010","journal-title":"Thin Solid Films"},{"key":"10.1016\/j.compchemeng.2025.109266_b87","first-page":"1","article-title":"Autonomous mobile robots for exploratory synthetic chemistry","author":"Dai","year":"2024","journal-title":"Nature"},{"key":"10.1016\/j.compchemeng.2025.109266_b88","article-title":"Machine learning in process systems engineering: Challenges and opportunities","author":"Daoutidis","year":"2023","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b89","series-title":"Darktrace","author":"Darktrace","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b90","series-title":"ORGANA: A robotic assistant for automated chemistry experimentation and characterization","author":"Darvish","year":"2024"},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b91","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1137\/S1052623496307510","article-title":"Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems","volume":"8","author":"Das","year":"1998","journal-title":"SIAM J. Optim."},{"key":"10.1016\/j.compchemeng.2025.109266_b92","series-title":"Opportunities and challenges in explainable artificial intelligence (xai): A survey","author":"Das","year":"2020"},{"key":"10.1016\/j.compchemeng.2025.109266_b93","first-page":"12760","article-title":"Bayesian optimization over discrete and mixed spaces via probabilistic reparameterization","volume":"35","author":"Daulton","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b94","doi-asserted-by":"crossref","first-page":"5454","DOI":"10.1038\/s41467-022-32938-1","article-title":"Autonomous optimization of non-aqueous li-ion battery electrolytes via robotic experimentation and machine learning coupling","volume":"13","author":"Dave","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compchemeng.2025.109266_b95","series-title":"Invited Keynote Paper, GL2-2, the Third China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems, Kanazawa, Japan","first-page":"34","article-title":"Multiobjective optimization: History and promise","volume":"vol. 2","author":"De Weck","year":"2004"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b96","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b97","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1038\/s41524-023-01115-3","article-title":"A deep learning framework to emulate density functional theory","volume":"9","author":"del Rio","year":"2023","journal-title":"Npj Comput. Mater."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b98","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1177\/2211068212460237","article-title":"Clarity: an open-source manager for laboratory automation","volume":"18","author":"Delaney","year":"2013","journal-title":"J. Lab. Autom."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b99","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202370014","article-title":"Research acceleration in self-driving labs: Technological roadmap toward accelerated materials and molecular discovery","volume":"5","author":"Delgado-Licona","year":"2023","journal-title":"Adv. Intell. Syst."},{"key":"10.1016\/j.compchemeng.2025.109266_b100","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1557\/s43577-021-00051-1","article-title":"Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer","volume":"46","author":"Deneault","year":"2021","journal-title":"MRS Bull."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b101","first-page":"3873","article-title":"Parallelizing exploration-exploitation tradeoffs in Gaussian process bandit optimization","volume":"15","author":"Desautels","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b102","series-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"10.1016\/j.compchemeng.2025.109266_b103","series-title":"Chain-of-verification reduces hallucination in large language models","author":"Dhuliawala","year":"2023"},{"key":"10.1016\/j.compchemeng.2025.109266_b104","first-page":"1","article-title":"Active learning and Bayesian optimization: a unified perspective to learn with a goal","author":"Di Fiore","year":"2024","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b105","series-title":"Adam: A method for stochastic optimization","author":"Diederik","year":"2014"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b106","doi-asserted-by":"crossref","first-page":"17809","DOI":"10.1038\/s41598-022-21646-x","article-title":"Artificial intelligence for online characterization of ultrashort x-ray free-electron laser pulses","volume":"12","author":"Dingel","year":"2022","journal-title":"Sci. Rep."},{"issue":"8","key":"10.1016\/j.compchemeng.2025.109266_b107","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1039\/D4DD00062E","article-title":"Operator-free HPLC automated method development guided by Bayesian optimization","volume":"3","author":"Dixon","year":"2024","journal-title":"Digit. Discov."},{"key":"10.1016\/j.compchemeng.2025.109266_b108","article-title":"High-dimensional Gaussian process bandits","volume":"26","author":"Djolonga","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compchemeng.2025.109266_b109","series-title":"Multi-fidelity Bayesian optimization in engineering design","author":"Do","year":"2023"},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b110","doi-asserted-by":"crossref","first-page":"4007","DOI":"10.1021\/acs.iecr.9b04496","article-title":"Handling of solids and flow characterization in a baffleless oscillatory flow coil reactor","volume":"59","author":"Doyle","year":"2019","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b111","doi-asserted-by":"crossref","DOI":"10.1016\/j.bej.2022.108764","article-title":"When bioprocess engineering meets machine learning: A survey from the perspective of automated bioprocess development","volume":"190","author":"Duong-Trung","year":"2023","journal-title":"Biochem. Eng. J."},{"key":"10.1016\/j.compchemeng.2025.109266_b112","article-title":"Bofire: bayesian optimization framework intended for real experiments","author":"D\u00fcrholt","year":"2024","journal-title":"arXiv preprint arXiv:2408.05040"},{"issue":"36","key":"10.1016\/j.compchemeng.2025.109266_b113","doi-asserted-by":"crossref","first-page":"10955","DOI":"10.1002\/ange.201705721","article-title":"Human versus robots in the discovery and crystallization of gigantic polyoxometalates","volume":"129","author":"Duros","year":"2017","journal-title":"Angew. Chem."},{"key":"10.1016\/j.compchemeng.2025.109266_b114","series-title":"Robust Optimization with Multiple Ranges and Chance Constraints","author":"Duzgun","year":"2012"},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b115","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3561048","article-title":"Explainable AI (XAI): Core ideas, techniques, and solutions","volume":"55","author":"Dwivedi","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.compchemeng.2025.109266_b116","series-title":"MHS\u201995. Proceedings of the Sixth International Symposium on Micro Machine and Human Science","first-page":"39","article-title":"A new optimizer using particle swarm theory","author":"Eberhart","year":"1995"},{"issue":"39","key":"10.1016\/j.compchemeng.2025.109266_b117","doi-asserted-by":"crossref","first-page":"8065","DOI":"10.1021\/acs.jpca.0c05006","article-title":"Multifidelity statistical machine learning for molecular crystal structure prediction","volume":"124","author":"Egorova","year":"2020","journal-title":"J. Phys. Chem. A"},{"issue":"31","key":"10.1016\/j.compchemeng.2025.109266_b118","doi-asserted-by":"crossref","first-page":"13940","DOI":"10.1021\/acs.iecr.0c02549","article-title":"High-throughput experimentation in olefin polymerization catalysis: Facing the challenges of miniaturization","volume":"59","author":"Ehm","year":"2020","journal-title":"Ind. Eng. Chem. Res."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b119","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trechm.2023.10.008","article-title":"Balancing act: when to flex and when to stay fixed","volume":"6","author":"El-khawaldeh","year":"2024","journal-title":"Trends Chem."},{"key":"10.1016\/j.compchemeng.2025.109266_b120","series-title":"ELPRO","author":"ELPRO","year":"2024"},{"issue":"30","key":"10.1016\/j.compchemeng.2025.109266_b121","article-title":"Artificial chemist: an autonomous quantum dot synthesis bot","volume":"32","author":"Epps","year":"2020","journal-title":"Adv. Mater."},{"issue":"23","key":"10.1016\/j.compchemeng.2025.109266_b122","doi-asserted-by":"crossref","first-page":"4040","DOI":"10.1039\/C7LC00884H","article-title":"Automated microfluidic platform for systematic studies of colloidal perovskite nanocrystals: towards continuous nano-manufacturing","volume":"17","author":"Epps","year":"2017","journal-title":"Lab A Chip"},{"issue":"17","key":"10.1016\/j.compchemeng.2025.109266_b123","doi-asserted-by":"crossref","first-page":"6025","DOI":"10.1039\/D0SC06463G","article-title":"Accelerated AI development for autonomous materials synthesis in flow","volume":"12","author":"Epps","year":"2021","journal-title":"Chem. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b124","article-title":"Scalable global optimization via local Bayesian optimization","volume":"32","author":"Eriksson","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"42","key":"10.1016\/j.compchemeng.2025.109266_b125","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.abf7435","article-title":"Accelerated discovery of 3D printing materials using data-driven multiobjective optimization","volume":"7","author":"Erps","year":"2021","journal-title":"Sci. Adv."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b126","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1002\/bit.28575","article-title":"Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics","volume":"121","author":"Espinel-R\u00edos","year":"2024","journal-title":"Biotechnol. Bioeng."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b127","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1038\/s41929-022-00744-z","article-title":"Interpretable machine learning for knowledge generation in heterogeneous catalysis","volume":"5","author":"Esterhuizen","year":"2022","journal-title":"Nat. Catal."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b128","doi-asserted-by":"crossref","first-page":"814","DOI":"10.3390\/app10030814","article-title":"Evobot: An open-source, modular, liquid handling robot for scientific experiments","volume":"10","author":"Fai\u00f1a","year":"2020","journal-title":"Appl. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b129","series-title":"2022 International Conference on Robotics and Automation","first-page":"6013","article-title":"Archemist: Autonomous robotic chemistry system architecture","author":"Fakhruldeen","year":"2022"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b130","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1038\/s41524-022-00947-9","article-title":"A multi-fidelity machine learning approach to high throughput materials screening","volume":"8","author":"Fare","year":"2022","journal-title":"Npj Comput. Mater."},{"issue":"7","key":"10.1016\/j.compchemeng.2025.109266_b131","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1039\/D0RE00081G","article-title":"Self-optimising processes and real-time-optimisation of organic syntheses in a microreactor system using Nelder\u2013Mead and design of experiments","volume":"5","author":"Fath","year":"2020","journal-title":"React. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b132","series-title":"AlabOS: A python-based reconfigurable workflow management framework for autonomous laboratories","author":"Fei","year":"2024"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b133","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1002\/cmtd.202000051","article-title":"Summit: benchmarking machine learning methods for reaction optimisation","volume":"1","author":"Felton","year":"2021","journal-title":"Chem.-Methods"},{"key":"10.1016\/j.compchemeng.2025.109266_b134","series-title":"Review of multi-fidelity models","author":"Fern\u00e1ndez-Godino","year":"2016"},{"key":"10.1016\/j.compchemeng.2025.109266_b135","series-title":"Figshare, credit for all your research","author":"Figshare","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b136","series-title":"Optimizing drug design by merging generative ai with active learning frameworks","author":"Filella-Merce","year":"2023"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b137","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1021\/acs.oprd.5b00313","article-title":"A novel internet-based reaction monitoring, control and autonomous self-optimization platform for chemical synthesis","volume":"20","author":"Fitzpatrick","year":"2016","journal-title":"Org. Process. Res. Dev."},{"issue":"46","key":"10.1016\/j.compchemeng.2025.109266_b138","doi-asserted-by":"crossref","first-page":"15128","DOI":"10.1002\/anie.201809080","article-title":"Across-the-world automated optimization and continuous-flow synthesis of pharmaceutical agents operating through a cloud-based server","volume":"57","author":"Fitzpatrick","year":"2018","journal-title":"Angew. Chem. Int. Ed."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b139","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1093\/comjnl\/6.2.163","article-title":"A rapidly convergent descent method for minimization","volume":"6","author":"Fletcher","year":"1963","journal-title":"Comput. J."},{"key":"10.1016\/j.compchemeng.2025.109266_b140","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2023.108194","article-title":"Combining multi-fidelity modelling and asynchronous batch Bayesian optimization","volume":"172","author":"Folch","year":"2023","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b141","series-title":"NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World","article-title":"Practical path-based Bayesian optimization","author":"Folch","year":"2023"},{"key":"10.1016\/j.compchemeng.2025.109266_b142","first-page":"35226","article-title":"SnAKe: Bayesian optimization with pathwise exploration","volume":"35","author":"Folch","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compchemeng.2025.109266_b143","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1613\/jair.1129","article-title":"PDDL2. 1: An extension to PDDL for expressing temporal planning domains","volume":"20","author":"Fox","year":"2003","journal-title":"J. Artificial Intelligence Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b144","series-title":"How can chemicals industry R&D leaders address disruption and keep innovating?","author":"Frank Jenner","year":"2023"},{"key":"10.1016\/j.compchemeng.2025.109266_b145","first-page":"45","article-title":"Bayesian optimization for materials design","author":"Frazier","year":"2016","journal-title":"Inf. Sci. Mater. Discov. Des."},{"key":"10.1016\/j.compchemeng.2025.109266_b146","series-title":"Batched Bayesian optimization with correlated candidate uncertainties","author":"Fromer","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b147","series-title":"Modifications of the DIRECT Algorithm","author":"Gablonsky","year":"2001"},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b148","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.ejor.2013.09.036","article-title":"Recent advances in robust optimization: An overview","volume":"235","author":"Gabrel","year":"2014","journal-title":"European J. Oper. Res."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b149","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1039\/D3DD00117B","article-title":"Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon\/krypton separations","volume":"2","author":"Gantzler","year":"2023","journal-title":"Digit. Discov."},{"key":"10.1016\/j.compchemeng.2025.109266_b150","series-title":"Empowering biomedical discovery with ai agents","author":"Gao","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b151","series-title":"Generative artificial intelligence for navigating synthesizable chemical space","author":"Gao","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b152","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.neucom.2019.11.004","article-title":"Dealing with categorical and integer-valued variables in Bayesian optimization with Gaussian processes","volume":"380","author":"Garrido-Merch\u00e1n","year":"2020","journal-title":"Neurocomputing"},{"issue":"10","key":"10.1016\/j.compchemeng.2025.109266_b153","doi-asserted-by":"crossref","first-page":"3543","DOI":"10.1021\/ie302069q","article-title":"Review of recent research on data-based process monitoring","volume":"52","author":"Ge","year":"2013","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b154","series-title":"Constrained Bayesian Optimization and Applications","author":"Gelbart","year":"2015"},{"key":"10.1016\/j.compchemeng.2025.109266_b155","series-title":"Bayesian optimization with unknown constraints","author":"Gelbart","year":"2014"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b156","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s13321-020-00472-1","article-title":"AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning","volume":"12","author":"Genheden","year":"2020","journal-title":"J. Cheminformatics"},{"key":"10.1016\/j.compchemeng.2025.109266_b157","series-title":"Computational Intelligence in Expensive Optimization Problems","first-page":"131","article-title":"Kriging is well-suited to parallelize optimization","author":"Ginsbourger","year":"2010"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b158","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","article-title":"Automatic chemical design using a data-driven continuous representation of molecules","volume":"4","author":"G\u00f3mez-Bombarelli","year":"2018","journal-title":"ACS Cent. Sci."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b159","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2021.102262","article-title":"Using simulation to accelerate autonomous experimentation: A case study using mechanics","volume":"24","author":"Gongora","year":"2021","journal-title":"Iscience"},{"issue":"15","key":"10.1016\/j.compchemeng.2025.109266_b160","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.aaz1708","article-title":"A Bayesian experimental autonomous researcher for mechanical design","volume":"6","author":"Gongora","year":"2020","journal-title":"Sci. Adv."},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b161","doi-asserted-by":"crossref","first-page":"3484","DOI":"10.1002\/aic.15411","article-title":"Outer approximation algorithm with physical domain reduction for computer-aided molecular and separation process design","volume":"62","author":"Gopinath","year":"2016","journal-title":"AIChE J."},{"key":"10.1016\/j.compchemeng.2025.109266_b162","series-title":"GPyOpt: A Bayesian optimization framework in python","author":"GPyOpt","year":"2016"},{"issue":"7714","key":"10.1016\/j.compchemeng.2025.109266_b163","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1038\/s41586-018-0307-8","article-title":"Controlling an organic synthesis robot with machine learning to search for new reactivity","volume":"559","author":"Granda","year":"2018","journal-title":"Nature"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b164","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MCI.2022.3155308","article-title":"Deep evolutionary learning for molecular design","volume":"17","author":"Grantham","year":"2022","journal-title":"IEEE Comput. Intell. Mag."},{"issue":"11","key":"10.1016\/j.compchemeng.2025.109266_b165","doi-asserted-by":"crossref","DOI":"10.1002\/adma.202004831","article-title":"Integrating computational and experimental workflows for accelerated organic materials discovery","volume":"33","author":"Greenaway","year":"2021","journal-title":"Adv. Mater."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b166","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1039\/D1SC05677H","article-title":"Multi-fidelity prediction of molecular optical peaks with deep learning","volume":"13","author":"Greenman","year":"2022","journal-title":"Chem. Sci."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b167","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1039\/C9SC04026A","article-title":"Constrained Bayesian optimization for automatic chemical design using variational autoencoders","volume":"11","author":"Griffiths","year":"2020","journal-title":"Chem. Sci."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b168","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.trechm.2019.07.004","article-title":"Universal chemical synthesis and discovery with \u2018the chemputer\u2019","volume":"2","author":"Gromski","year":"2020","journal-title":"Trends Chem."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b169","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1038\/s41570-018-0066-y","article-title":"How to explore chemical space using algorithms and automation","volume":"3","author":"Gromski","year":"2019","journal-title":"Nat. Rev. Chem."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b170","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1017\/S1431927621000386","article-title":"Machine learning pipeline for segmentation and defect identification from high-resolution transmission electron microscopy data","volume":"27","author":"Groschner","year":"2021","journal-title":"Microsc. Microanal."},{"key":"10.1016\/j.compchemeng.2025.109266_b171","series-title":"Documentation PostgreSQL 10.3","author":"Group","year":"2018"},{"issue":"24","key":"10.1016\/j.compchemeng.2025.109266_b172","doi-asserted-by":"crossref","DOI":"10.1063\/1.5005095","article-title":"Machine learning of molecular properties: Locality and active learning","volume":"148","author":"Gubaev","year":"2018","journal-title":"J. Chem. Phys."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b173","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1039\/D3DD00166K","article-title":"Orchestrating nimble experiments across interconnected labs","volume":"2","author":"Guevarra","year":"2023","journal-title":"Digit. Discov."},{"issue":"44","key":"10.1016\/j.compchemeng.2025.109266_b174","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.adj0461","article-title":"AI-driven robotic chemist for autonomous synthesis of organic molecules","volume":"9","author":"Ha","year":"2023","journal-title":"Sci. Adv."},{"issue":"44","key":"10.1016\/j.compchemeng.2025.109266_b175","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.adj0461","article-title":"AI-driven robotic chemist for autonomous synthesis of organic molecules","volume":"9","author":"Ha","year":"2023","journal-title":"Sci. Adv."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b176","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1177\/2472630319860775","article-title":"Integrated robotic mini bioreactor platform for automated, parallel microbial cultivation with online data handling and process control","volume":"24","author":"Haby","year":"2019","journal-title":"SLAS Technol.: Transl. Life Sci. Innov."},{"key":"10.1016\/j.compchemeng.2025.109266_b177","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.cie.2016.11.001","article-title":"Flexible job shop scheduling problem with parallel batch processing machines: MIP and CP approaches","volume":"102","author":"Ham","year":"2016","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b178","doi-asserted-by":"crossref","unstructured":"Hamano, R., Saito, S., Nomura, M., Uchida, K., Shirakawa, S., 2024. CatCMA: Stochastic Optimization for Mixed-Category Problems. In: Proceedings of the Genetic and Evolutionary Computation Conference. pp. 656\u2013664.","DOI":"10.1145\/3638529.3654198"},{"key":"10.1016\/j.compchemeng.2025.109266_b179","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compchemeng.2025.109266_b180","series-title":"Automated liquid handling equipment\u2014Hamiltonian","author":"Hamiltonian","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b181","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.psep.2022.07.019","article-title":"XFDDC: explainable fault detection diagnosis and correction framework for chemical process systems","volume":"165","author":"Harinarayan","year":"2022","journal-title":"Process. Saf. Environ. Prot."},{"key":"10.1016\/j.compchemeng.2025.109266_b182","article-title":"Autonomous synthesis of thin film materials with pulsed laser deposition enabled by in situ spectroscopy and automation","author":"Harris","year":"2024","journal-title":"Small Methods"},{"key":"10.1016\/j.compchemeng.2025.109266_b183","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.coche.2020.05.002","article-title":"Flow chemistry remains an opportunity for chemists and chemical engineers","volume":"29","author":"Hartman","year":"2020","journal-title":"Curr. Opin. Chem. Eng."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b184","doi-asserted-by":"crossref","DOI":"10.1063\/5.0048164","article-title":"Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge","volume":"8","author":"H\u00e4se","year":"2021","journal-title":"Appl. Phys. Rev."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b185","article-title":"Olympus: a benchmarking framework for noisy optimization and experiment planning","volume":"2","author":"H\u00e4se","year":"2021","journal-title":"Mach. Learn.: Sci. Technol."},{"issue":"39","key":"10.1016\/j.compchemeng.2025.109266_b186","doi-asserted-by":"crossref","first-page":"7642","DOI":"10.1039\/C8SC02239A","article-title":"Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories","volume":"9","author":"H\u00e4se","year":"2018","journal-title":"Chem. Sci."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b187","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.trechm.2019.02.007","article-title":"Next-generation experimentation with self-driving laboratories","volume":"1","author":"H\u00e4se","year":"2019","journal-title":"Trends Chem."},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b188","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1021\/acscentsci.8b00307","article-title":"Phoenics: a Bayesian optimizer for chemistry","volume":"4","author":"Hase","year":"2018","journal-title":"ACS Cent. Sci."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b189","article-title":"An active learning approach with uncertainty, representativeness, and diversity","volume":"2014","author":"He","year":"2014","journal-title":"Sci. World J."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b190","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1021\/acs.jcim.3c01250","article-title":"Chemprop: a machine learning package for chemical property prediction","volume":"64","author":"Heid","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compchemeng.2025.109266_b191","series-title":"International Conference on Machine Learning","first-page":"1699","article-title":"Predictive entropy search for Bayesian optimization with unknown constraints","author":"Hern\u00e1ndez-Lobato","year":"2015"},{"key":"10.1016\/j.compchemeng.2025.109266_b192","series-title":"International Conference on Machine Learning","first-page":"1470","article-title":"Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space","author":"Hern\u00e1ndez-Lobato","year":"2017"},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b193","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1039\/D2DD00028H","article-title":"Bayesian optimization with known experimental and design constraints for chemistry applications","volume":"1","author":"Hickman","year":"2022","journal-title":"Digit. Discov."},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b194","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1039\/D3RE00008G","article-title":"Equipping data-driven experiment planning for self-driving laboratories with semantic memory: case studies of transfer learning in chemical reaction optimization","volume":"8","author":"Hickman","year":"2023","journal-title":"React. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b195","doi-asserted-by":"crossref","DOI":"10.1039\/D4DD00115J","article-title":"Atlas: a brain for self-driving laboratories","author":"Hickman","year":"2025","journal-title":"Digit. Discov."},{"key":"10.1016\/j.compchemeng.2025.109266_b196","series-title":"Atlas: a brain for self-driving laboratories","author":"Hickman","year":"2023"},{"key":"10.1016\/j.compchemeng.2025.109266_b197","doi-asserted-by":"crossref","DOI":"10.1039\/D5DD00018A","article-title":"Anubis: bayesian optimization with unknown feasibility constraints for scientific experimentation","author":"Hickman","year":"2025","journal-title":"Digit. Discov."},{"issue":"21","key":"10.1016\/j.compchemeng.2025.109266_b198","article-title":"Data-driven materials science: status, challenges, and perspectives","volume":"6","author":"Himanen","year":"2019","journal-title":"Adv. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b199","article-title":"Adaptation in naturai and artificial systerns","author":"Holland","year":"1975","journal-title":"Ann Arbor"},{"issue":"11","key":"10.1016\/j.compchemeng.2025.109266_b200","doi-asserted-by":"crossref","first-page":"6064","DOI":"10.1021\/acs.cgd.1c00231","article-title":"Droplet-based evaporative system for the estimation of protein crystallization kinetics","volume":"21","author":"Hong","year":"2021","journal-title":"Cryst. Growth Des."},{"key":"10.1016\/j.compchemeng.2025.109266_b201","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s00158-005-0587-0","article-title":"Sequential kriging optimization using multiple-fidelity evaluations","volume":"32","author":"Huang","year":"2006","journal-title":"Struct. Multidiscip. Optim."},{"issue":"16","key":"10.1016\/j.compchemeng.2025.109266_b202","doi-asserted-by":"crossref","first-page":"10001","DOI":"10.1021\/acs.chemrev.0c01303","article-title":"Ab initio machine learning in chemical compound space","volume":"121","author":"Huang","year":"2021","journal-title":"Chem. Rev."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b203","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1377612.1377613","article-title":"SNOBFIT\u2013stable noisy optimization by branch and fit","volume":"35","author":"Huyer","year":"2008","journal-title":"ACM Trans. Math. Softw. (TOMS)"},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b204","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1039\/D4DD00040D","article-title":"The future of self-driving laboratories: from human in the loop interactive AI to gamification","volume":"3","author":"Hysmith","year":"2024","journal-title":"Digit. Discov."},{"key":"10.1016\/j.compchemeng.2025.109266_b205","series-title":"IBM_QRadar","author":"IBM","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b206","series-title":"Computer Aided Chemical Engineering","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1016\/B978-0-443-28824-1.50218-0","article-title":"Computer-aided molecular and process design (CAMPD) for ionic liquid assisted extractive distillation of refrigerant mixtures","volume":"vol. 53","author":"Iftakher","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b207","series-title":"Spaya","author":"Iktos","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b208","series-title":"EasySampler 1210 system, complete\u2014Mettler0-Toledo international inc","author":"Inc.","year":"2024"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b209","article-title":"Autonomous experimental systems in materials science","volume":"3","author":"Ishizuki","year":"2023","journal-title":"Sci. Technol. Adv. Mater.: Methods"},{"key":"10.1016\/j.compchemeng.2025.109266_b210","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s12065-019-00246-1","article-title":"Chemical reaction optimization: survey on variants","volume":"12","author":"Islam","year":"2019","journal-title":"Evol. Intell."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b211","doi-asserted-by":"crossref","first-page":"120","DOI":"10.3103\/S002713492202045X","article-title":"The any light particle search experiment at DESY","volume":"77","author":"Isleif","year":"2022","journal-title":"Mosc. Univ. Phys. Bull."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b212","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1038\/s42256-023-00788-1","article-title":"Leveraging large language models for predictive chemistry","volume":"6","author":"Jablonka","year":"2024","journal-title":"Nat. Mach. Intell."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b213","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1080\/03052159808941372","article-title":"Discrete manufacturing process design optimization using computer simulation and generalized hill climbing algorithms","volume":"31","author":"Jacobson","year":"1998","journal-title":"Eng. Optim."},{"key":"10.1016\/j.compchemeng.2025.109266_b214","article-title":"Explainable artificial intelligence for fault diagnosis of industrial processes","author":"Jang","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.compchemeng.2025.109266_b215","series-title":"2006 IEEE International Conference on Evolutionary Computation","first-page":"2814","article-title":"Improving evolution strategies through active covariance matrix adaptation","author":"Jastrebski","year":"2006"},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b216","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1021\/acs.chemrestox.1c00410","article-title":"Vapor pressure and toxicity prediction for novichok agent candidates using machine learning model: preparation for unascertained nerve agents after chemical weapons convention schedule 1 update","volume":"35","author":"Jeong","year":"2022","journal-title":"Chem. Res. Toxicol."},{"issue":"40","key":"10.1016\/j.compchemeng.2025.109266_b217","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.abo2626","article-title":"An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials","volume":"8","author":"Jiang","year":"2022","journal-title":"Sci. Adv."},{"key":"10.1016\/j.compchemeng.2025.109266_b218","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2021.107547","article-title":"Using ATR-FTIR spectra and convolutional neural networks for characterizing mixed plastic waste","volume":"155","author":"Jiang","year":"2021","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b219","series-title":"International Conference on Machine Learning","first-page":"2323","article-title":"Junction tree variational autoencoder for molecular graph generation","author":"Jin","year":"2018"},{"key":"10.1016\/j.compchemeng.2025.109266_b220","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/BF00941892","article-title":"Lipschitzian optimization without the Lipschitz constant","volume":"79","author":"Jones","year":"1993","journal-title":"J. Optim. Theory Appl."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b221","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1021\/ie0301684","article-title":"Fault detection using canonical variate analysis","volume":"43","author":"Juricek","year":"2004","journal-title":"Ind. Eng. Chem. Res."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b222","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1093\/mictod\/qaad096","article-title":"Human-in-the-loop: The future of machine learning in automated electron microscopy","volume":"32","author":"Kalinin","year":"2024","journal-title":"Microsc. Today"},{"key":"10.1016\/j.compchemeng.2025.109266_b223","series-title":"International Conference on Machine Learning","first-page":"1799","article-title":"Multi-fidelity Bayesian optimisation with continuous approximations","author":"Kandasamy","year":"2017"},{"key":"10.1016\/j.compchemeng.2025.109266_b224","series-title":"International Conference on Machine Learning","first-page":"295","article-title":"High dimensional Bayesian optimisation and bandits via additive models","author":"Kandasamy","year":"2015"},{"issue":"81","key":"10.1016\/j.compchemeng.2025.109266_b225","first-page":"1","article-title":"Tuning hyperparameters without grad students: Scalable and robust Bayesian optimisation with dragonfly","volume":"21","author":"Kandasamy","year":"2020","journal-title":"J. Mach. Learn. Res."},{"issue":"1\u20132","key":"10.1016\/j.compchemeng.2025.109266_b226","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.compchemeng.2007.07.005","article-title":"Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry","volume":"32","author":"Kano","year":"2008","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b227","doi-asserted-by":"crossref","DOI":"10.1016\/j.ohx.2022.e00319","article-title":"Sidekick: a low-cost open-source 3D-printed liquid dispensing robot","volume":"12","author":"Keesey","year":"2022","journal-title":"HardwareX"},{"key":"10.1016\/j.compchemeng.2025.109266_b228","unstructured":"Kenton, J.D.M.-W.C., Toutanova, L.K., 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NaacL-HLT. vol. 1, Minneapolis, Minnesota, p. 2."},{"key":"10.1016\/j.compchemeng.2025.109266_b229","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2022.138443","article-title":"Machine learning directed multi-objective optimization of mixed variable chemical systems","volume":"451","author":"Kershaw","year":"2023","journal-title":"Chem. Eng. J."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b230","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3146389","article-title":"On fault detection and diagnosis in robotic systems","volume":"51","author":"Khalastchi","year":"2018","journal-title":"ACM Comput. Surv."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b231","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1090\/S0002-9939-1953-0055639-3","article-title":"Sequential minimax search for a maximum","volume":"4","author":"Kiefer","year":"1953","journal-title":"Proc. Amer. Math. Soc."},{"key":"10.1016\/j.compchemeng.2025.109266_b232","series-title":"Learning to warm-start Bayesian hyperparameter optimization","author":"Kim","year":"2017"},{"issue":"7","key":"10.1016\/j.compchemeng.2025.109266_b233","first-page":"934","article-title":"Model predictive control guided with optimal experimental design for pulse-based parallel cultivation","volume":"55","author":"Kim","year":"2022","journal-title":"IFAC-Pap."},{"key":"10.1016\/j.compchemeng.2025.109266_b234","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2023.108158","article-title":"Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of E. coli","volume":"172","author":"Kim","year":"2023","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b235","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2022.108004","article-title":"Primal\u2013dual differential dynamic programming: A model-based reinforcement learning for constrained dynamic optimization","volume":"167","author":"Kim","year":"2022","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b236","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2021.107465","article-title":"Model-based reinforcement learning and predictive control for two-stage optimal control of fed-batch bioreactor","volume":"154","author":"Kim","year":"2021","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b237","series-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"10.1016\/j.compchemeng.2025.109266_b238","series-title":"Research task forces","author":"KIWI-biolab","year":"2024"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b239","doi-asserted-by":"crossref","first-page":"6151","DOI":"10.1038\/s41467-022-33879-5","article-title":"Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA","volume":"13","author":"Klukowski","year":"2022","journal-title":"Nat. Commun."},{"issue":"11","key":"10.1016\/j.compchemeng.2025.109266_b240","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1039\/D2PY00040G","article-title":"Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation","volume":"13","author":"Knox","year":"2022","journal-title":"Polym. Chem."},{"key":"10.1016\/j.compchemeng.2025.109266_b241","doi-asserted-by":"crossref","DOI":"10.1039\/D5PY00123D","article-title":"Self-driving laboratory platform for many-objective self-optimisation of polymer nanoparticle synthesis with cloud-integrated machine learning and orthogonal online analytics","author":"Knox","year":"2025","journal-title":"Polym. Chem."},{"key":"10.1016\/j.compchemeng.2025.109266_b242","series-title":"MySQL 5","author":"Kofler","year":"2005"},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b243","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1002\/nme.1646","article-title":"Parallel asynchronous particle swarm optimization","volume":"67","author":"Koh","year":"2006","journal-title":"Internat. J. Numer. Methods Engrg."},{"issue":"6677","key":"10.1016\/j.compchemeng.2025.109266_b244","doi-asserted-by":"crossref","DOI":"10.1126\/science.adi1407","article-title":"Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back","volume":"382","author":"Koscher","year":"2023","journal-title":"Science"},{"issue":"12","key":"10.1016\/j.compchemeng.2025.109266_b245","doi-asserted-by":"crossref","first-page":"3584","DOI":"10.1002\/bit.28236","article-title":"High-throughput screening of optimal process conditions using model predictive control","volume":"119","author":"Krausch","year":"2022","journal-title":"Biotechnol. Bioeng."},{"key":"10.1016\/j.compchemeng.2025.109266_b246","series-title":"KUKA lab robots sort 3000 blood samples daily","author":"KUKA","year":"2025"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b247","article-title":"Making machine learning a useful tool in the accelerated discovery of transition metal complexes","volume":"10","author":"Kulik","year":"2020","journal-title":"Wiley Interdiscip. Rev.: Comput. Mol. Sci."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b248","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1038\/s41467-020-19597-w","article-title":"On-the-fly closed-loop materials discovery via Bayesian active learning","volume":"11","author":"Kusne","year":"2020","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b249","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-019-0396-x","article-title":"Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation","volume":"11","author":"Kwon","year":"2019","journal-title":"J. Cheminf."},{"key":"10.1016\/j.compchemeng.2025.109266_b250","series-title":"Liquid handling\u2014SPT LabTech","author":"LabTech","year":"2024"},{"issue":"43","key":"10.1016\/j.compchemeng.2025.109266_b251","doi-asserted-by":"crossref","first-page":"17835","DOI":"10.1021\/acs.iecr.3c02520","article-title":"Artificial intelligence (AI) workflow for catalyst design and optimization","volume":"62","author":"Lai","year":"2023","journal-title":"Ind. Eng. Chem. Res."},{"issue":"21","key":"10.1016\/j.compchemeng.2025.109266_b252","doi-asserted-by":"crossref","first-page":"8821","DOI":"10.1021\/ie5006542","article-title":"Simultaneous optimization of working fluid and process for organic rankine cycles using PC-SAFT","volume":"53","author":"Lampe","year":"2014","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b253","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.compchemeng.2015.04.008","article-title":"Computer-aided molecular design in the continuous-molecular targeting framework using group-contribution PC-SAFT","volume":"81","author":"Lampe","year":"2015","journal-title":"Comput. Chem. Eng."},{"issue":"14","key":"10.1016\/j.compchemeng.2025.109266_b254","article-title":"Beyond ternary OPV: high-throughput experimentation and self-driving laboratories optimize multicomponent systems","volume":"32","author":"Langner","year":"2020","journal-title":"Adv. Mater."},{"key":"10.1016\/j.compchemeng.2025.109266_b255","series-title":"29th IEEE Conference on Decision and Control","first-page":"596","article-title":"Canonical variate analysis in identification, filtering, and adaptive control","author":"Larimore","year":"1990"},{"key":"10.1016\/j.compchemeng.2025.109266_b256","doi-asserted-by":"crossref","DOI":"10.1016\/j.conengprac.2024.105841","article-title":"Machine learning for industrial sensing and control: A survey and practical perspective","volume":"145","author":"Lawrence","year":"2024","journal-title":"Control Eng. Pract."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b257","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1916461.1916468","article-title":"Algorithm 909: NOMAD: Nonlinear optimization with the MADS algorithm","volume":"37","author":"Le Digabel","year":"2011","journal-title":"ACM Trans. Math. Softw. (TOMS)"},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b258","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1039\/C8RE00323H","article-title":"Continuous generation, in-line quantification and utilization of nitrosyl chloride in photonitrosation reactions","volume":"4","author":"Lebl","year":"2019","journal-title":"React. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b259","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2023.108204","article-title":"Enabling the direct solution of challenging computer-aided molecular and process design problems: Chemical absorption of carbon dioxide","volume":"174","author":"Lee","year":"2023","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b260","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2020.106802","article-title":"A comparative study of multi-objective optimization methodologies for molecular and process design","volume":"136","author":"Lee","year":"2020","journal-title":"Comput. Chem. Eng."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b261","doi-asserted-by":"crossref","DOI":"10.1103\/PRXEnergy.3.011002","article-title":"Challenges in high-throughput inorganic materials prediction and autonomous synthesis","volume":"3","author":"Leeman","year":"2024","journal-title":"PRX Energy"},{"key":"10.1016\/j.compchemeng.2025.109266_b262","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-020-00458-z","article-title":"EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation","volume":"12","author":"Leguy","year":"2020","journal-title":"J. Cheminformatics"},{"issue":"8","key":"10.1016\/j.compchemeng.2025.109266_b263","doi-asserted-by":"crossref","first-page":"3108","DOI":"10.1021\/acs.oprd.3c00397","article-title":"Autonomous online optimization in flash chemistry using online mass spectrometry","volume":"28","author":"Lehmann","year":"2024","journal-title":"Org. Process. Res. Dev."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b264","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1039\/D2DD00061J","article-title":"Collaborative methods to enhance reproducibility and accelerate discovery","volume":"2","author":"Leins","year":"2023","journal-title":"Digit. Discov."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b265","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1038\/s41467-024-45444-3","article-title":"An integrated self-optimizing programmable chemical synthesis and reaction engine","volume":"15","author":"Leonov","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compchemeng.2025.109266_b266","first-page":"1546","article-title":"Re-examining linear embeddings for high-dimensional Bayesian optimization","volume":"33","author":"Letham","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b267","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1214\/18-BA1110","article-title":"Constrained Bayesian optimization with noisy experiments","volume":"14","author":"Letham","year":"2019","journal-title":"Bayesian Anal."},{"key":"10.1016\/j.compchemeng.2025.109266_b268","series-title":"Multi-fidelity methods for optimization: A survey","author":"Li","year":"2024"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b269","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1038\/s41467-020-15728-5","article-title":"Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab","volume":"11","author":"Li","year":"2020","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b270","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1038\/s41467-020-15728-5","article-title":"Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab","volume":"11","author":"Li","year":"2020","journal-title":"Nat. Commun."},{"issue":"13","key":"10.1016\/j.compchemeng.2025.109266_b271","doi-asserted-by":"crossref","first-page":"5650","DOI":"10.1021\/acs.chemmater.0c01153","article-title":"Robot-accelerated perovskite investigation and discovery","volume":"32","author":"Li","year":"2020","journal-title":"Chem. Mater."},{"key":"10.1016\/j.compchemeng.2025.109266_b272","article-title":"An order-invariant and interpretable dilated convolution neural network for chemical process fault detection and diagnosis","author":"Li","year":"2023","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b273","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/s13321-023-00732-w","article-title":"A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data","volume":"15","author":"Li","year":"2023","journal-title":"J. Cheminformatics"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b274","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1038\/s41524-021-00656-9","article-title":"Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains","volume":"7","author":"Liang","year":"2021","journal-title":"Npj Comput. Mater."},{"key":"10.1016\/j.compchemeng.2025.109266_b275","series-title":"International Conference on Intelligent Computing","first-page":"235","article-title":"Evolutionary optimization with dynamic fidelity computational models","author":"Lim","year":"2008"},{"key":"10.1016\/j.compchemeng.2025.109266_b276","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-018-0286-7","article-title":"Molecular generative model based on conditional variational autoencoder for de novo molecular design","volume":"10","author":"Lim","year":"2018","journal-title":"J. Cheminformatics"},{"key":"10.1016\/j.compchemeng.2025.109266_b277","doi-asserted-by":"crossref","unstructured":"Lin, Y.-S., Lee, W.-C., Celik, Z.B., 2021. What do you see? Evaluation of explainable artificial intelligence (XAI) interpretability through neural backdoors. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. pp. 1027\u20131035.","DOI":"10.1145\/3447548.3467213"},{"issue":"12","key":"10.1016\/j.compchemeng.2025.109266_b278","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.1039\/C9SC03666K","article-title":"Automatic retrosynthetic route planning using template-free models","volume":"11","author":"Lin","year":"2020","journal-title":"Chem. Sci."},{"issue":"54","key":"10.1016\/j.compchemeng.2025.109266_b279","first-page":"1","article-title":"SMAC3: A versatile Bayesian optimization package for hyperparameter optimization","volume":"23","author":"Lindauer","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b280","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s40192-017-0098-z","article-title":"High-dimensional materials and process optimization using data-driven experimental design with well-calibrated uncertainty estimates","volume":"6","author":"Ling","year":"2017","journal-title":"Integr. Mater. Manuf. Innov."},{"issue":"10","key":"10.1016\/j.compchemeng.2025.109266_b281","article-title":"The emergent role of explainable artificial intelligence in the materials sciences","volume":"4","author":"Liu","year":"2023","journal-title":"Cell Rep. Phys. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b282","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.knosys.2017.12.034","article-title":"Remarks on multi-output Gaussian process regression","volume":"144","author":"Liu","year":"2018","journal-title":"Knowl.-Based Syst."},{"issue":"10","key":"10.1016\/j.compchemeng.2025.109266_b283","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1021\/acscentsci.7b00303","article-title":"Retrosynthetic reaction prediction using neural sequence-to-sequence models","volume":"3","author":"Liu","year":"2017","journal-title":"ACS Cent. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b284","series-title":"Mind\u2019s eye: Grounded language model reasoning through simulation","author":"Liu","year":"2022"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b285","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.xphs.2021.09.011","article-title":"A fully integrated online platform for real time monitoring of multiple product quality attributes in biopharmaceutical processes for monoclonal antibody therapeutics","volume":"111","author":"Liu","year":"2022","journal-title":"J. Pharm. Sci."},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b286","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1039\/D3DD00223C","article-title":"Review of low-cost self-driving laboratories in chemistry and materials science: the \u201cfrugal twin\u201d concept","volume":"3","author":"Lo","year":"2024","journal-title":"Digit. Discov."},{"key":"10.1016\/j.compchemeng.2025.109266_b287","article-title":"Mapping pareto fronts for efficient multi-objective materials discovery","author":"Low","year":"2022","journal-title":"Authorea Prepr."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b288","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1038\/s41524-024-01274-x","article-title":"Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs","volume":"10","author":"Low","year":"2024","journal-title":"Npj Comput. Mater."},{"key":"10.1016\/j.compchemeng.2025.109266_b289","series-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b290","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"issue":"7","key":"10.1016\/j.compchemeng.2025.109266_b291","doi-asserted-by":"crossref","first-page":"2456","DOI":"10.1039\/D3SC06206F","article-title":"Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry","volume":"15","author":"Lunt","year":"2024","journal-title":"Chem. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b292","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.cjche.2024.01.019","article-title":"Causal temporal graph attention network for fault diagnosis of chemical processes","volume":"70","author":"Luo","year":"2024","journal-title":"Chin. J. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b293","first-page":"1","article-title":"Augmenting large language models with chemistry tools","author":"M. Bran","year":"2024","journal-title":"Nat. Mach. Intell."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b294","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s10589-023-00466-3","article-title":"Branch-and-model: a derivative-free global optimization algorithm","volume":"85","author":"Ma","year":"2023","journal-title":"Comput. Optim. Appl."},{"issue":"A5","key":"10.1016\/j.compchemeng.2025.109266_b295","article-title":"Design of optimal solvents for liquid-liquid extraction and gas absorption processes","volume":"68","author":"Macchietto","year":"1990","journal-title":"Chem. Eng. Res. Design;(United Kingdom)"},{"issue":"20","key":"10.1016\/j.compchemeng.2025.109266_b296","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.aaz8867","article-title":"Self-driving laboratory for accelerated discovery of thin-film materials","volume":"6","author":"MacLeod","year":"2020","journal-title":"Sci. Adv."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b297","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1038\/s41467-022-28580-6","article-title":"A self-driving laboratory advances the Pareto front for material properties","volume":"13","author":"MacLeod","year":"2022","journal-title":"Nat. Commun."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b298","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.mencom.2021.11.003","article-title":"Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow","volume":"31","author":"Madzhidov","year":"2021","journal-title":"Mendeleev Commun."},{"key":"10.1016\/j.compchemeng.2025.109266_b299","series-title":"2021 12th International Conference on Information, Intelligence, Systems & Applications","first-page":"1","article-title":"Bayesian optimization in high-dimensional spaces: A brief survey","author":"Malu","year":"2021"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b300","doi-asserted-by":"crossref","first-page":"5797","DOI":"10.1038\/s41467-021-25974-w","article-title":"Orchestrating and sharing large multimodal data for transparent and reproducible research","volume":"12","author":"Mammoliti","year":"2021","journal-title":"Nat. Commun."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b301","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s10898-021-01052-9","article-title":"MVMOO: mixed variable multi-objective optimisation","volume":"80","author":"Manson","year":"2021","journal-title":"J. Global Optim."},{"key":"10.1016\/j.compchemeng.2025.109266_b302","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-018-0263-1","article-title":"OPERA models for predicting physicochemical properties and environmental fate endpoints","volume":"10","author":"Mansouri","year":"2018","journal-title":"J. Cheminformatics"},{"issue":"11","key":"10.1016\/j.compchemeng.2025.109266_b303","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1038\/s41557-022-01016-w","article-title":"An autonomous portable platform for universal chemical synthesis","volume":"14","author":"Manzano","year":"2022","journal-title":"Nat. Chem."},{"issue":"10","key":"10.1016\/j.compchemeng.2025.109266_b304","doi-asserted-by":"crossref","first-page":"3403","DOI":"10.1021\/ie960096z","article-title":"Optimal computer-aided molecular design: A polymer design case study","volume":"35","author":"Maranas","year":"1996","journal-title":"Ind. Eng. Chem. Res."},{"issue":"11","key":"10.1016\/j.compchemeng.2025.109266_b305","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1557\/s43577-022-00466-4","article-title":"Artificial intelligence for materials research at extremes","volume":"47","author":"Maruyama","year":"2022","journal-title":"MRS Bull."},{"key":"10.1016\/j.compchemeng.2025.109266_b306","series-title":"Mathworks \u2014 Maker of matlab and simulink \u2014 mathworks.com","author":"MathWorks","year":"2025"},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b307","article-title":"Data-driven automated robotic experiments accelerate discovery of multi-component electrolyte for rechargeable Li\u2013O2 batteries","volume":"3","author":"Matsuda","year":"2022","journal-title":"Cell Rep. Phys. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b308","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2024.108660","article-title":"Mixed-integer optimisation of graph neural networks for computer-aided molecular design","volume":"185","author":"McDonald","year":"2024","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b309","series-title":"Making science better: Reproducibility, falsifiability and the scientific method","author":"McIntosh","year":"2019"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b310","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.2000.10485979","article-title":"A comparison of three methods for selecting values of input variables in the analysis of output from a computer code","volume":"42","author":"McKay","year":"2000","journal-title":"Technometrics"},{"key":"10.1016\/j.compchemeng.2025.109266_b311","doi-asserted-by":"crossref","DOI":"10.1016\/j.compositesb.2021.109160","article-title":"Investigations on explainable artificial intelligence methods for the deep learning classification of fibre layup defect in the automated composite manufacturing","volume":"224","author":"Meister","year":"2021","journal-title":"Compos. Part B: Eng."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b312","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1073\/pnas.1714936115","article-title":"Active learning machine learns to create new quantum experiments","volume":"115","author":"Melnikov","year":"2018","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b313","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1021\/acs.oprd.9b00140","article-title":"The evolution of high-throughput experimentation in pharmaceutical development and perspectives on the future","volume":"23","author":"Mennen","year":"2019","journal-title":"Org. Process. Res. Dev."},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b314","doi-asserted-by":"crossref","DOI":"10.1088\/0965-0393\/24\/5\/055001","article-title":"Multi-objective constrained design of nickel-base superalloys using data mining-and thermodynamics-driven genetic algorithms","volume":"24","author":"Menou","year":"2016","journal-title":"Modelling Simul. Mater. Sci. Eng."},{"issue":"7990","key":"10.1016\/j.compchemeng.2025.109266_b315","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1038\/s41586-023-06735-9","article-title":"Scaling deep learning for materials discovery","volume":"624","author":"Merchant","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.compchemeng.2025.109266_b316","series-title":"Augmented language models: a survey","author":"Mialon","year":"2023"},{"key":"10.1016\/j.compchemeng.2025.109266_b317","series-title":"A continuous relaxation for discrete Bayesian optimization","author":"Michael","year":"2024"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b318","doi-asserted-by":"crossref","first-page":"12546","DOI":"10.1038\/ncomms12546","article-title":"Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth","volume":"7","author":"Milias-Argeitis","year":"2016","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compchemeng.2025.109266_b319","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2024.108720","article-title":"A workflow management system for reproducible and interoperable high-throughput self-driving experiments","volume":"187","author":"Mione","year":"2024","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b320","doi-asserted-by":"crossref","DOI":"10.1016\/j.trac.2020.116045","article-title":"New data preprocessing trends based on ensemble of multiple preprocessing techniques","volume":"132","author":"Mishra","year":"2020","journal-title":"TRAC Trends Anal. Chem."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b321","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1287\/ijoc.2020.0993","article-title":"Mixed-integer convex nonlinear optimization with gradient-boosted trees embedded","volume":"33","author":"Mistry","year":"2021","journal-title":"INFORMS J. Comput."},{"issue":"10","key":"10.1016\/j.compchemeng.2025.109266_b322","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.1021\/acs.accounts.9b00399","article-title":"Machine learning applied to zeolite synthesis: the missing link for realizing high-throughput discovery","volume":"52","author":"Moliner","year":"2019","journal-title":"Acc. Chem. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b323","series-title":"Design and Analysis of Experiments","author":"Montgomery","year":"2017"},{"issue":"32","key":"10.1016\/j.compchemeng.2025.109266_b324","doi-asserted-by":"crossref","first-page":"8517","DOI":"10.1039\/D0SC01101K","article-title":"Autonomous intelligent agents for accelerated materials discovery","volume":"11","author":"Montoya","year":"2020","journal-title":"Chem. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b325","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1007\/s10994-020-05899-z","article-title":"High-dimensional Bayesian optimization using low-dimensional feature spaces","volume":"109","author":"Moriconi","year":"2020","journal-title":"Mach. Learn."},{"issue":"38","key":"10.1016\/j.compchemeng.2025.109266_b326","doi-asserted-by":"crossref","first-page":"20774","DOI":"10.1002\/ange.202102009","article-title":"Sampling and analysis in flow: the keys to smarter, more controllable, and sustainable fine-chemical manufacturing","volume":"133","author":"Morin","year":"2021","journal-title":"Angew. Chem."},{"issue":"35","key":"10.1016\/j.compchemeng.2025.109266_b327","doi-asserted-by":"crossref","first-page":"13185","DOI":"10.1021\/acssuschemeng.4c03592","article-title":"Self-driving synthesis of protein nanoparticles by active transfer-learning-assisted autonomous flow platform","volume":"12","author":"Mottafegh","year":"2024","journal-title":"ACS Sustain. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b328","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2021.107630","article-title":"Safe chance constrained reinforcement learning for batch process control","volume":"157","author":"Mowbray","year":"2022","journal-title":"Comput. Chem. Eng."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b329","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1002\/bit.28262","article-title":"A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification","volume":"120","author":"Mowbray","year":"2023","journal-title":"Biotechnol. Bioeng."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b330","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1039\/D1RE00549A","article-title":"Automated multi-objective reaction optimisation: which algorithm should I use?","volume":"7","author":"M\u00fcller","year":"2022","journal-title":"React. Chem. Eng."},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b331","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1016\/j.cor.2012.08.022","article-title":"SO-MI: A surrogate model algorithm for computationally expensive nonlinear mixed-integer black-box global optimization problems","volume":"40","author":"M\u00fcller","year":"2013","journal-title":"Comput. Oper. Res."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b332","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1039\/b008780g","article-title":"Development of chemical markup language (CML) as a system for handling complex chemical content","volume":"25","author":"Murray-Rust","year":"2001","journal-title":"New J. Chem."},{"key":"10.1016\/j.compchemeng.2025.109266_b333","article-title":"Efficient high dimensional Bayesian optimization with additivity and quadrature Fourier features","volume":"31","author":"Mutny","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compchemeng.2025.109266_b334","doi-asserted-by":"crossref","DOI":"10.1016\/j.actamat.2021.116987","article-title":"Deep learning-based discriminative refocusing of scanning electron microscopy images for materials science","volume":"214","author":"Na","year":"2021","journal-title":"Acta Mater."},{"key":"10.1016\/j.compchemeng.2025.109266_b335","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.energy.2017.03.047","article-title":"A modified DIRECT algorithm for hidden constraints in an LNG process optimization","volume":"126","author":"Na","year":"2017","journal-title":"Energy"},{"issue":"12","key":"10.1016\/j.compchemeng.2025.109266_b336","doi-asserted-by":"crossref","DOI":"10.2533\/chimia.2019.997","article-title":"Data-driven chemical reaction prediction and retrosynthesis","volume":"73","author":"Nair","year":"2019","journal-title":"Chimia"},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b337","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1093\/comjnl\/7.4.308","article-title":"A simplex method for function minimization","volume":"7","author":"Nelder","year":"1965","journal-title":"Comput. J."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b338","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1002\/elsc.201200021","article-title":"Consistent development of bioprocesses from microliter cultures to the industrial scale","volume":"13","author":"Neubauer","year":"2013","journal-title":"Eng. Life Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b339","doi-asserted-by":"crossref","unstructured":"Nguyen, D., Gupta, S., Rana, S., Shilton, A., Venkatesh, S., 2020. Bayesian optimization for categorical and category-specific continuous inputs. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 5256\u20135263, 04.","DOI":"10.1609\/aaai.v34i04.5971"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b340","doi-asserted-by":"crossref","first-page":"17663","DOI":"10.1038\/s41598-020-74394-1","article-title":"Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels","volume":"10","author":"Noack","year":"2020","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b341","doi-asserted-by":"crossref","first-page":"11809","DOI":"10.1038\/s41598-019-48114-3","article-title":"A kriging-based approach to autonomous experimentation with applications to x-ray scattering","volume":"9","author":"Noack","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compchemeng.2025.109266_b342","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/978-0-387-40065-5_4","article-title":"Trust-region methods","author":"Nocedal","year":"2006","journal-title":"Numer. Optim."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b343","doi-asserted-by":"crossref","first-page":"2757","DOI":"10.1038\/s41467-024-47070-5","article-title":"An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations","volume":"15","author":"Noh","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compchemeng.2025.109266_b344","series-title":"General chemically intuitive atom-and bond-level DFT descriptors for machine learning approaches to reaction condition prediction","author":"Nouman","year":"2024"},{"issue":"8","key":"10.1016\/j.compchemeng.2025.109266_b345","first-page":"966","article-title":"RoboDiff: combining a sample changer and goniometer for highly automated macromolecular crystallography experiments","volume":"72","author":"Nurizzo","year":"2016","journal-title":"Biol. Crystallogr."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b346","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/TII.2009.2032654","article-title":"Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations","volume":"6","author":"Odiowei","year":"2009","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b347","doi-asserted-by":"crossref","DOI":"10.1002\/aic.17658","article-title":"Integration of reinforcement learning and model predictive control to optimize semi-batch bioreactor","volume":"68","author":"Oh","year":"2022","journal-title":"AIChE J."},{"issue":"46","key":"10.1016\/j.compchemeng.2025.109266_b348","doi-asserted-by":"crossref","first-page":"11476","DOI":"10.1021\/acs.jpclett.1c03291","article-title":"Interpretable machine learning of chemical bonding at solid surfaces","volume":"12","author":"Omidvar","year":"2021","journal-title":"J. Phys. Chem. Lett."},{"key":"10.1016\/j.compchemeng.2025.109266_b349","series-title":"Lab autonmation\u2014Opentrons","author":"Opentrons","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b350","series-title":"Oracle java documentation: Collections framework overview","author":"Oracle and\/or its affiliates","year":"2015"},{"key":"10.1016\/j.compchemeng.2025.109266_b351","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b352","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1021\/accountsmr.1c00244","article-title":"Interpretable and explainable machine learning for materials science and chemistry","volume":"3","author":"Oviedo","year":"2022","journal-title":"Accounts Mater. Res."},{"issue":"7","key":"10.1016\/j.compchemeng.2025.109266_b353","doi-asserted-by":"crossref","first-page":"2755","DOI":"10.1021\/acs.oprd.4c00110","article-title":"Automated continuous crystallization platform with real-time particle size analysis via laser diffraction","volume":"28","author":"Pal","year":"2024","journal-title":"Org. Process. Res. Dev."},{"key":"10.1016\/j.compchemeng.2025.109266_b354","series-title":"Panasonic","author":"Panasonic","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b355","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jprocont.2018.07.013","article-title":"Control of a bioreactor using a new partially supervised reinforcement learning algorithm","volume":"69","author":"Pandian","year":"2018","journal-title":"J. Process Control"},{"issue":"12","key":"10.1016\/j.compchemeng.2025.109266_b356","doi-asserted-by":"crossref","first-page":"3000","DOI":"10.1039\/D3RE00156C","article-title":"Autonomous kinetic model identification using optimal experimental design and retrospective data analysis: methane complete oxidation as a case study","volume":"8","author":"Pankajakshan","year":"2023","journal-title":"React. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b357","doi-asserted-by":"crossref","DOI":"10.1016\/j.jelechem.2023.117972","article-title":"Accelerated stress tests for pt\/c electrocatalysts: An approach to understanding the degradation mechanisms","volume":"952","author":"Paperzh","year":"2024","journal-title":"J. Electroanal. Chem."},{"key":"10.1016\/j.compchemeng.2025.109266_b358","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1007\/s00158-016-1550-y","article-title":"Remarks on multi-fidelity surrogates","volume":"55","author":"Park","year":"2017","journal-title":"Struct. Multidiscip. Optim."},{"key":"10.1016\/j.compchemeng.2025.109266_b359","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1007\/s00158-018-2031-2","article-title":"Low-fidelity scale factor improves Bayesian multi-fidelity prediction by reducing bumpiness of discrepancy function","volume":"58","author":"Park","year":"2018","journal-title":"Struct. Multidiscip. Optim."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b360","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1080\/00207179.2017.1323351","article-title":"Stochastic model predictive control with joint chance constraints","volume":"93","author":"Paulson","year":"2020","journal-title":"Internat. J. Control"},{"key":"10.1016\/j.compchemeng.2025.109266_b361","series-title":"Bayesian optimization as a flexible and efficient design framework for sustainable process systems","author":"Paulson","year":"2024"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b362","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.nima.2008.01.072","article-title":"Measurement of the real time fill-pattern at the Australian synchrotron","volume":"589","author":"Peake","year":"2008","journal-title":"Nucl. Instrum. Methods Phys. Res. Sect. A: Accel. Spectrometers, Detect. Assoc. Equip."},{"key":"10.1016\/j.compchemeng.2025.109266_b363","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.neunet.2015.09.001","article-title":"Smart sampling and incremental function learning for very large high dimensional data","volume":"78","author":"Pedergnana","year":"2016","journal-title":"Neural Netw."},{"key":"10.1016\/j.compchemeng.2025.109266_b364","series-title":"Check your facts and try again: Improving large language models with external knowledge and automated feedback","author":"Peng","year":"2023"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b365","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1557\/s43577-023-00481-z","article-title":"Next-generation intelligent laboratories for materials design and manufacturing","volume":"48","author":"Peng","year":"2023","journal-title":"MRS Bull."},{"issue":"7985","key":"10.1016\/j.compchemeng.2025.109266_b366","first-page":"16","article-title":"Can AI-run labs speed up science?","volume":"625","author":"Peters","year":"2025","journal-title":"Nature"},{"key":"10.1016\/j.compchemeng.2025.109266_b367","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2019.106649","article-title":"Reinforcement learning for batch bioprocess optimization","volume":"133","author":"Petsagkourakis","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b368","doi-asserted-by":"crossref","DOI":"10.1016\/j.commatsci.2021.110360","article-title":"Machine learning in materials science: From explainable predictions to autonomous design","volume":"193","author":"Pilania","year":"2021","journal-title":"Comput. Mater. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b369","series-title":"2022 International Joint Conference on Neural Networks","first-page":"1","article-title":"Solis: Autonomous solubility screening using deep neural networks","author":"Pizzuto","year":"2022"},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b370","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s10898-021-01085-0","article-title":"Review and comparison of algorithms and software for mixed-integer derivative-free optimization","volume":"82","author":"Ploskas","year":"2022","journal-title":"J. Global Optim."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b371","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1021\/acs.accounts.0c00785","article-title":"Data-driven strategies for accelerated materials design","volume":"54","author":"Pollice","year":"2021","journal-title":"Acc. Chem. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b372","series-title":"2016 Winter Simulation Conference","first-page":"770","article-title":"Warm starting Bayesian optimization","author":"Poloczek","year":"2016"},{"issue":"12","key":"10.1016\/j.compchemeng.2025.109266_b373","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1021\/acs.oprd.3c00422","article-title":"Sulfur tetrafluoride (SF4) as a deoxyfluorination reagent for organic synthesis in continuous flow mode","volume":"27","author":"Polterauer","year":"2023","journal-title":"Org. Process. Res. Dev."},{"key":"10.1016\/j.compchemeng.2025.109266_b374","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2020.565644","article-title":"Molecular sets (MOSES): a benchmarking platform for molecular generation models","volume":"11","author":"Polykovskiy","year":"2020","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.compchemeng.2025.109266_b375","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2022.139099","article-title":"Automated ph adjustment driven by robotic workflows and active machine learning","volume":"451","author":"Pomberger","year":"2023","journal-title":"Chem. Eng. J."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b376","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1039\/D2RE00008C","article-title":"The effect of chemical representation on active machine learning towards closed-loop optimization","volume":"7","author":"Pomberger","year":"2022","journal-title":"React. Chem. Eng."},{"issue":"28","key":"10.1016\/j.compchemeng.2025.109266_b377","doi-asserted-by":"crossref","first-page":"11352","DOI":"10.1002\/ange.202000329","article-title":"An autonomous chemical robot discovers the rules of inorganic coordination chemistry without prior knowledge","volume":"132","author":"Porwol","year":"2020","journal-title":"Angew. Chem."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b378","doi-asserted-by":"crossref","first-page":"151","DOI":"10.3390\/bioengineering7040151","article-title":"The promise of optogenetics for bioproduction: dynamic control strategies and scale-up instruments","volume":"7","author":"Pouzet","year":"2020","journal-title":"Bioengineering"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b379","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1109\/TPWRS.2015.2417204","article-title":"Efficient estimation of the probability of small-disturbance instability of large uncertain power systems","volume":"31","author":"Preece","year":"2015","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.compchemeng.2025.109266_b380","doi-asserted-by":"crossref","unstructured":"Provost, F., Jensen, D., Oates, T., 1999. Efficient progressive sampling. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 23\u201332.","DOI":"10.1145\/312129.312188"},{"key":"10.1016\/j.compchemeng.2025.109266_b381","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-019-0397-9","article-title":"A de novo molecular generation method using latent vector based generative adversarial network","volume":"11","author":"Prykhodko","year":"2019","journal-title":"J. Cheminformatics"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b382","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202570005","article-title":"Self-driving lab for solid-phase extraction process optimization and application to nucleic acid purification","volume":"7","author":"Putz","year":"2025","journal-title":"Adv. Intell. Syst."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b383","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1038\/s41524-022-00765-z","article-title":"Accelerating materials discovery using artificial intelligence, high performance computing and robotics","volume":"8","author":"Pyzer-Knapp","year":"2022","journal-title":"Npj Comput. Mater."},{"key":"10.1016\/j.compchemeng.2025.109266_b384","series-title":"Improving language understanding with unsupervised learning","author":"Radford","year":"2018"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b385","doi-asserted-by":"crossref","first-page":"3423","DOI":"10.1038\/s41467-021-23831-4","article-title":"Deep learning connects DNA traces to transcription to reveal predictive features beyond enhancer\u2013promoter contact","volume":"12","author":"Rajpurkar","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compchemeng.2025.109266_b386","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.swevo.2016.09.002","article-title":"Noisy evolutionary optimization algorithms\u2013a comprehensive survey","volume":"33","author":"Rakshit","year":"2017","journal-title":"Swarm Evol. Comput."},{"key":"10.1016\/j.compchemeng.2025.109266_b387","series-title":"Goal-Driven Learning","author":"Ram","year":"1995"},{"issue":"11","key":"10.1016\/j.compchemeng.2025.109266_b388","first-page":"13191","article-title":"Ultrasonic or microwave modified continuous flow chemistry for the synthesis of tetrahydrocannabinol: observing effects of various solvents and acids","volume":"9","author":"Ramirez","year":"2024","journal-title":"ACS Omega"},{"key":"10.1016\/j.compchemeng.2025.109266_b389","series-title":"A review of large language models and autonomous agents in chemistry","author":"Ramos","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b390","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.spl.2014.07.032","article-title":"Space-filling latin hypercube designs based on randomization restrictions in factorial experiments","volume":"94","author":"Ranjan","year":"2014","journal-title":"Statist. Probab. Lett."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b391","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/s44286-023-00002-4","article-title":"Self-driving laboratories to autonomously navigate the protein fitness landscape","volume":"1","author":"Rapp","year":"2024","journal-title":"Nat. Chem. Eng."},{"issue":"42","key":"10.1016\/j.compchemeng.2025.109266_b392","doi-asserted-by":"crossref","first-page":"17677","DOI":"10.1021\/jacs.1c08181","article-title":"Machine-learning-guided discovery of 19F MRI agents enabled by automated copolymer synthesis","volume":"143","author":"Reis","year":"2021","journal-title":"J. Am. Chem. Soc."},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b393","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1021\/acs.accounts.6b00261","article-title":"Feedback in flow for accelerated reaction development","volume":"49","author":"Reizman","year":"2016","journal-title":"Acc. Chem. Res."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b394","article-title":"High-throughput experimental techniques for corrosion research: A review","volume":"1","author":"Ren","year":"2023","journal-title":"Mater. Genome Eng. Adv."},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b395","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1038\/s41578-023-00588-4","article-title":"Autonomous experiments using active learning and AI","volume":"8","author":"Ren","year":"2023","journal-title":"Nat. Rev. Mater."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b396","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1287\/ijoc.1100.0419","article-title":"Enhancement of sandwich algorithms for approximating higher-dimensional convex Pareto sets","volume":"23","author":"Rennen","year":"2011","journal-title":"INFORMS J. Comput."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b397","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1021\/acs.accounts.2c00525","article-title":"Advancements in liquid jet technology and X-ray spectroscopy for understanding energy conversion materials during operation","volume":"56","author":"Reuss","year":"2023","journal-title":"Acc. Chem. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b398","article-title":"An automated electrochemical flow platform to accelerate library synthesis and reaction optimization","author":"Rial-Rodr\u00edguez","year":"2024","journal-title":"Angew. Chem."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b399","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1039\/D2DD00067A","article-title":"Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory","volume":"2","author":"Rieger","year":"2023","journal-title":"Digit. Discov."},{"key":"10.1016\/j.compchemeng.2025.109266_b400","doi-asserted-by":"crossref","DOI":"10.1021\/jacs.4c10244","article-title":"Crystal structure determination from powder diffraction patterns with generative machine learning","author":"Riesel","year":"2024","journal-title":"J. Am. Chem. Soc."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b401","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1007\/s10898-012-9951-y","article-title":"Derivative-free optimization: a review of algorithms and comparison of software implementations","volume":"56","author":"Rios","year":"2013","journal-title":"J. Global Optim."},{"key":"10.1016\/j.compchemeng.2025.109266_b402","series-title":"Smoothllm: Defending large language models against jailbreaking attacks","author":"Robey","year":"2023"},{"key":"10.1016\/j.compchemeng.2025.109266_b403","series-title":"CHRONECT XPR robotic powder and liquid dispensing","author":"Robotics","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b404","series-title":"Lab automation-ABB robotics","author":"robotics","year":"2025"},{"issue":"10","key":"10.1016\/j.compchemeng.2025.109266_b405","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1007\/s10822-020-00314-0","article-title":"Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions","volume":"34","author":"Rodr\u00edguez-P\u00e9rez","year":"2020","journal-title":"J. Comput. Aided Mol. Des."},{"issue":"24","key":"10.1016\/j.compchemeng.2025.109266_b406","doi-asserted-by":"crossref","first-page":"17744","DOI":"10.1021\/acs.jmedchem.1c01789","article-title":"Explainable machine learning for property predictions in compound optimization: miniperspective","volume":"64","author":"Rodr\u00edguez-P\u00e9rez","year":"2021","journal-title":"J. Med. Chem."},{"issue":"10","key":"10.1016\/j.compchemeng.2025.109266_b407","doi-asserted-by":"crossref","first-page":"2696","DOI":"10.1039\/C9SC05999G","article-title":"Benchmarking the acceleration of materials discovery by sequential learning","volume":"11","author":"Rohr","year":"2020","journal-title":"Chem. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b408","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"298","article-title":"High-dimensional Bayesian optimization via additive models with overlapping groups","author":"Rolland","year":"2018"},{"key":"10.1016\/j.compchemeng.2025.109266_b409","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b410","doi-asserted-by":"crossref","first-page":"10160","DOI":"10.1038\/s41467-024-54457-x","article-title":"An automatic end-to-end chemical synthesis development platform powered by large language models","volume":"15","author":"Ruan","year":"2024","journal-title":"Nat. Commun."},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b411","article-title":"A self-driving laboratory optimizes a scalable process for making functional coatings","volume":"4","author":"Rupnow","year":"2023","journal-title":"Cell Rep. Phys. Sci."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b412","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/S0169-7439(00)00058-7","article-title":"Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis","volume":"51","author":"Russell","year":"2000","journal-title":"Chemometr. Intell. Lab. Syst."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b413","doi-asserted-by":"crossref","DOI":"10.1002\/btm2.10627","article-title":"Advances in high throughput cell culture technologies for therapeutic screening and biological discovery applications","volume":"9","author":"Ryoo","year":"2024","journal-title":"Bioeng. Transl. Med."},{"key":"10.1016\/j.compchemeng.2025.109266_b414","doi-asserted-by":"crossref","unstructured":"Saini, A., Prasad, R., 2022. Select wisely and explain: Active learning and probabilistic local post-hoc explainability. In: Proceedings of the 2022 AAAI\/ACM Conference on AI, Ethics, and Society. pp. 599\u2013608.","DOI":"10.1145\/3514094.3534191"},{"issue":"21","key":"10.1016\/j.compchemeng.2025.109266_b415","doi-asserted-by":"crossref","first-page":"9821","DOI":"10.3390\/app11219821","article-title":"Lab scale implementation of industry 4.0 for an automatic yogurt filling production system\u2014experimentation, modeling and process optimization","volume":"11","author":"Salah","year":"2021","journal-title":"Appl. Sci."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b416","doi-asserted-by":"crossref","first-page":"2771","DOI":"10.1038\/s41467-020-16501-4","article-title":"A nanomaterials discovery robot for the darwinian evolution of shape programmable gold nanoparticles","volume":"11","author":"Salley","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compchemeng.2025.109266_b417","series-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","first-page":"29","article-title":"Material microstructure design using VAE-regression with a multimodal prior","author":"Sardeshmukh","year":"2024"},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b418","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s42979-021-00557-0","article-title":"Ai-driven cybersecurity: an overview, security intelligence modeling and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b419","doi-asserted-by":"crossref","DOI":"10.1016\/j.dmpk.2021.100401","article-title":"Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design","volume":"39","author":"Sasahara","year":"2021","journal-title":"Drug Metab. Pharmacokinet."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b420","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1039\/C7ME00125H","article-title":"COSMO-CAMPD: a framework for integrated design of molecules and processes based on COSMO-RS","volume":"3","author":"Scheffczyk","year":"2018","journal-title":"Mol. Syst. Des. Eng."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b421","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s41929-024-01275-5","article-title":"Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis","volume":"8","author":"Scheurer","year":"2025","journal-title":"Nat. Catal."},{"key":"10.1016\/j.compchemeng.2025.109266_b422","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.egypro.2017.09.184","article-title":"Integrated design of ORC process and working fluid using process flowsheeting software and PC-SAFT","volume":"129","author":"Schilling","year":"2017","journal-title":"Energy Procedia"},{"key":"10.1016\/j.compchemeng.2025.109266_b423","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.ces.2016.04.048","article-title":"1-stage CoMT-CAMD: An approach for integrated design of ORC process and working fluid using PC-SAFT","volume":"159","author":"Schilling","year":"2017","journal-title":"Chem. Eng. Sci."},{"issue":"17","key":"10.1016\/j.compchemeng.2025.109266_b424","doi-asserted-by":"crossref","first-page":"5791","DOI":"10.1016\/j.ces.2006.04.001","article-title":"A new approach for sequential experimental design for model discrimination","volume":"61","author":"Schwaab","year":"2006","journal-title":"Chem. Eng. Sci."},{"issue":"28","key":"10.1016\/j.compchemeng.2025.109266_b425","doi-asserted-by":"crossref","first-page":"6091","DOI":"10.1039\/C8SC02339E","article-title":"\u201cFound in translation\u201d: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models","volume":"9","author":"Schwaller","year":"2018","journal-title":"Chem. Sci."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b426","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1038\/s42256-020-00284-w","article-title":"Mapping the space of chemical reactions using attention-based neural networks","volume":"3","author":"Schwaller","year":"2021","journal-title":"Nat. Mach. Intell."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b427","article-title":"Prediction of chemical reaction yields using deep learning","volume":"2","author":"Schwaller","year":"2021","journal-title":"Mach. Learn.: Sci. Technol."},{"key":"10.1016\/j.compchemeng.2025.109266_b428","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.cej.2018.07.031","article-title":"Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives","volume":"352","author":"Schweidtmann","year":"2018","journal-title":"Chem. Eng. J."},{"key":"10.1016\/j.compchemeng.2025.109266_b429","article-title":"A review and perspective on hybrid modeling methodologies","volume":"10","author":"Schweidtmann","year":"2024","journal-title":"Digit. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b430","series-title":"Autonomous materials discovery and optimization: Proceedings of a workshop\u2013in brief","author":"National Academies of Sciences","year":"2023"},{"issue":"17","key":"10.1016\/j.compchemeng.2025.109266_b431","doi-asserted-by":"crossref","first-page":"2454","DOI":"10.1021\/acs.accounts.2c00220","article-title":"Autonomous chemical experiments: Challenges and perspectives on establishing a self-driving lab","volume":"55","author":"Seifrid","year":"2022","journal-title":"Acc. Chem. Res."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b432","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1146\/annurev-chembioeng-060816-101411","article-title":"High-throughput automation in chemical process development","volume":"8","author":"Selekman","year":"2017","journal-title":"Annu. Rev. Chem. Biomol. Eng."},{"key":"10.1016\/j.compchemeng.2025.109266_b433","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.arcontrol.2016.09.001","article-title":"Perspectives on process monitoring of industrial systems","volume":"42","author":"Severson","year":"2016","journal-title":"Annu. Rev. Control."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b434","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","article-title":"Taking the human out of the loop: A review of Bayesian optimization","volume":"104","author":"Shahriari","year":"2015","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.compchemeng.2025.109266_b435","series-title":"Handbook of Materials Characterization","author":"Sharma","year":"2018"},{"key":"10.1016\/j.compchemeng.2025.109266_b436","doi-asserted-by":"crossref","unstructured":"Shen, D., Zhang, J., Su, J., Zhou, G., Tan, C.L., 2004. Multi-criteria-based active learning for named entity recognition. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics. ACL-04, pp. 589\u2013596.","DOI":"10.3115\/1218955.1219030"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b437","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1038\/s41467-024-47210-x","article-title":"Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation","volume":"15","author":"Sheng","year":"2024","journal-title":"Nat. Commun."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b438","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1021\/acsmedchemlett.7b00165","article-title":"Practical high-throughput experimentation for chemists","volume":"8","author":"Shevlin","year":"2017","journal-title":"ACS Med. Chem. Lett."},{"issue":"26","key":"10.1016\/j.compchemeng.2025.109266_b439","doi-asserted-by":"crossref","first-page":"9959","DOI":"10.1021\/acs.analchem.3c01101","article-title":"1D gradient-weighted class activation mapping, visualizing decision process of convolutional neural network-based models in spectroscopy analysis","volume":"95","author":"Shi","year":"2023","journal-title":"Anal. Chem."},{"issue":"7844","key":"10.1016\/j.compchemeng.2025.109266_b440","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/s41586-021-03213-y","article-title":"Bayesian reaction optimization as a tool for chemical synthesis","volume":"590","author":"Shields","year":"2021","journal-title":"Nature"},{"key":"10.1016\/j.compchemeng.2025.109266_b441","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.ress.2015.12.002","article-title":"The generalization of latin hypercube sampling","volume":"148","author":"Shields","year":"2016","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"40","key":"10.1016\/j.compchemeng.2025.109266_b442","doi-asserted-by":"crossref","first-page":"8253","DOI":"10.1021\/acs.jpca.3c04779","article-title":"Recent applications of machine learning in molecular property and chemical reaction outcome predictions","volume":"127","author":"Shilpa","year":"2023","journal-title":"J. Phys. Chem. A"},{"issue":"22","key":"10.1016\/j.compchemeng.2025.109266_b443","doi-asserted-by":"crossref","first-page":"6655","DOI":"10.1039\/D1SC06932B","article-title":"Predicting reaction conditions from limited data through active transfer learning","volume":"13","author":"Shim","year":"2022","journal-title":"Chem. Sci."},{"issue":"12","key":"10.1016\/j.compchemeng.2025.109266_b444","doi-asserted-by":"crossref","first-page":"3659","DOI":"10.1021\/acs.jcim.3c00577","article-title":"Machine learning strategies for reaction development: toward the low-data limit","volume":"63","author":"Shim","year":"2023","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b445","first-page":"15","article-title":"Development of an improved scheduling algorithm for lab test operations on a small-size bio robot platform","volume":"15","author":"Shin","year":"2010","journal-title":"JALA: J. Assoc. Lab. Autom."},{"key":"10.1016\/j.compchemeng.2025.109266_b446","doi-asserted-by":"crossref","first-page":"2158","DOI":"10.1021\/acscatal.4c05530","article-title":"Discovering the origin of catalyst performance and degradation of electrochemical CO2 reduction through interpretable machine learning","volume":"15","author":"Shin","year":"2025","journal-title":"ACS Catal."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b447","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2021.102176","article-title":"Automated solubility screening platform using computer vision","volume":"24","author":"Shiri","year":"2021","journal-title":"Iscience"},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b448","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.1016\/j.matt.2024.04.022","article-title":"Chemos 2.0: An orchestration architecture for chemical self-driving laboratories","volume":"7","author":"Sim","year":"2024","journal-title":"Matter"},{"issue":"6681","key":"10.1016\/j.compchemeng.2025.109266_b449","doi-asserted-by":"crossref","DOI":"10.1126\/science.adj1817","article-title":"Automated self-optimization, intensification, and scale-up of photocatalysis in flow","volume":"383","author":"Slattery","year":"2024","journal-title":"Science"},{"issue":"6681","key":"10.1016\/j.compchemeng.2025.109266_b450","doi-asserted-by":"crossref","DOI":"10.1126\/science.adj1817","article-title":"Automated self-optimization, intensification, and scale-up of photocatalysis in flow","volume":"383","author":"Slattery","year":"2024","journal-title":"Science"},{"key":"10.1016\/j.compchemeng.2025.109266_b451","series-title":"Measurements with noise: Bayesian optimization for co-optimizing noise and property discovery in automated experiments","author":"Slautin","year":"2024"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b452","doi-asserted-by":"crossref","first-page":"4290","DOI":"10.1038\/s41467-024-48534-4","article-title":"Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership","volume":"15","author":"Snapp","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compchemeng.2025.109266_b453","article-title":"Practical Bayesian optimization of machine learning algorithms","volume":"25","author":"Snoek","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b454","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1016\/j.ejor.2006.06.046","article-title":"Ant colony optimization for continuous domains","volume":"185","author":"Socha","year":"2008","journal-title":"European J. Oper. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b455","series-title":"A multiagent-driven robotic ai chemist enabling autonomous chemical research on demand","author":"Song","year":"2025"},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b456","doi-asserted-by":"crossref","DOI":"10.1002\/cmtd.202100091","article-title":"Comparison of derivative-free algorithms for their applicability in self-optimization of chemical processes","volume":"2","author":"Soritz","year":"2022","journal-title":"Chem.-Methods"},{"key":"10.1016\/j.compchemeng.2025.109266_b457","series-title":"Final Safety Assessment Document for Proton Facilities","author":"Spallation Neutron Source (SNS)","year":"2010"},{"issue":"35","key":"10.1016\/j.compchemeng.2025.109266_b458","doi-asserted-by":"crossref","first-page":"5316","DOI":"10.1039\/D1CC07035E","article-title":"Modern machine learning for tackling inverse problems in chemistry: molecular design to realization","volume":"58","author":"Sridharan","year":"2022","journal-title":"Chem. Commun."},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b459","doi-asserted-by":"crossref","first-page":"2702","DOI":"10.1016\/j.matt.2021.06.036","article-title":"Autonomous experimentation systems for materials development: A community perspective","volume":"4","author":"Stach","year":"2021","journal-title":"Matter"},{"issue":"42","key":"10.1016\/j.compchemeng.2025.109266_b460","doi-asserted-by":"crossref","first-page":"9640","DOI":"10.1039\/C9SC03766G","article-title":"Progress and prospects for accelerating materials science with automated and autonomous workflows","volume":"10","author":"Stein","year":"2019","journal-title":"Chem. Sci."},{"issue":"6423","key":"10.1016\/j.compchemeng.2025.109266_b461","doi-asserted-by":"crossref","DOI":"10.1126\/science.aav2211","article-title":"Organic synthesis in a modular robotic system driven by a chemical programming language","volume":"363","author":"Steiner","year":"2019","journal-title":"Science"},{"key":"10.1016\/j.compchemeng.2025.109266_b462","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2021.107381","article-title":"Adding interpretability to predictive maintenance by machine learning on sensor data","volume":"152","author":"Steurtewagen","year":"2021","journal-title":"Comput. Chem. Eng."},{"issue":"6697","key":"10.1016\/j.compchemeng.2025.109266_b463","doi-asserted-by":"crossref","DOI":"10.1126\/science.adk9227","article-title":"Delocalized, asynchronous, closed-loop discovery of organic laser emitters","volume":"384","author":"Strieth-Kalthoff","year":"2024","journal-title":"Science"},{"issue":"16","key":"10.1016\/j.compchemeng.2025.109266_b464","first-page":"11005","article-title":"Artificial intelligence for retrosynthetic planning needs both data and expert knowledge","volume":"146","author":"Strieth-Kalthoff","year":"2024","journal-title":"J. Am. Chem. Soc."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b465","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1007\/s11590-021-01726-z","article-title":"A new DIRECT-GLh algorithm for global optimization with hidden constraints","volume":"15","author":"Stripinis","year":"2021","journal-title":"Optim. Lett."},{"key":"10.1016\/j.compchemeng.2025.109266_b466","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.coche.2019.11.006","article-title":"Derivative-free optimization for chemical product design","volume":"27","author":"Sun","year":"2020","journal-title":"Curr. Opin. Chem. Eng."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b467","doi-asserted-by":"crossref","first-page":"5844","DOI":"10.1038\/s41467-024-50215-1","article-title":"Active learning streamlines development of high performance catalysts for higher alcohol synthesis","volume":"15","author":"Suvarna","year":"2024","journal-title":"Nat. Commun."},{"issue":"7990","key":"10.1016\/j.compchemeng.2025.109266_b468","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1038\/s41586-023-06734-w","article-title":"An autonomous laboratory for the accelerated synthesis of novel materials","volume":"624","author":"Szymanski","year":"2023","journal-title":"Nature"},{"issue":"8","key":"10.1016\/j.compchemeng.2025.109266_b469","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1039\/D1MH00495F","article-title":"Toward autonomous design and synthesis of novel inorganic materials","volume":"8","author":"Szymanski","year":"2021","journal-title":"Mater. Horizons"},{"issue":"20","key":"10.1016\/j.compchemeng.2025.109266_b470","doi-asserted-by":"crossref","first-page":"5904","DOI":"10.1002\/anie.201506101","article-title":"Computer-assisted synthetic planning: the end of the beginning","volume":"55","author":"Szymku\u0107","year":"2016","journal-title":"Angew. Chem. Int. Ed."},{"key":"10.1016\/j.compchemeng.2025.109266_b471","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11721-016-0125-2","article-title":"A new particle swarm optimization algorithm for noisy optimization problems","volume":"10","author":"Taghiyeh","year":"2016","journal-title":"Swarm Intell."},{"key":"10.1016\/j.compchemeng.2025.109266_b472","series-title":"2019 International Joint Conference on Neural Networks","first-page":"1","article-title":"Active learning with interpretable predictor","author":"Taguchi","year":"2019"},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b473","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.201900126","article-title":"Automation of controlled\/living radical polymerization","volume":"2","author":"Tamasi","year":"2020","journal-title":"Adv. Intell. Syst."},{"issue":"30","key":"10.1016\/j.compchemeng.2025.109266_b474","article-title":"Machine learning on a robotic platform for the design of polymer\u2013protein hybrids","volume":"34","author":"Tamasi","year":"2022","journal-title":"Adv. Mater."},{"issue":"21","key":"10.1016\/j.compchemeng.2025.109266_b475","doi-asserted-by":"crossref","first-page":"9992","DOI":"10.1021\/acs.jpcc.3c00765","article-title":"A surrogate machine learning model for the design of single-atom catalyst on carbon and porphyrin supports towards electrochemistry","volume":"127","author":"Tamtaji","year":"2023","journal-title":"J. Phys. Chem. C"},{"key":"10.1016\/j.compchemeng.2025.109266_b476","series-title":"ChemAgent: Self-updating library in large language models improves chemical reasoning","author":"Tang","year":"2025"},{"issue":"51","key":"10.1016\/j.compchemeng.2025.109266_b477","doi-asserted-by":"crossref","DOI":"10.1002\/adfm.202106725","article-title":"Self-driving platform for metal nanoparticle synthesis: combining microfluidics and machine learning","volume":"31","author":"Tao","year":"2021","journal-title":"Adv. Funct. Mater."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b478","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1002\/cben.202000027","article-title":"A review on data-driven learning approaches for fault detection and diagnosis in chemical processes","volume":"8","author":"Taqvi","year":"2021","journal-title":"ChemBioEng Rev."},{"key":"10.1016\/j.compchemeng.2025.109266_b479","series-title":"Liquid handling and robotics\u2014Tecan","author":"Tecan","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b480","series-title":"Freedom EVO platform\u2014Tecan","author":"Tecan","year":"2025"},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b481","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s00366-014-0372-z","article-title":"Initial sampling methods in metamodel-assisted optimization","volume":"31","author":"Tenne","year":"2015","journal-title":"Eng. Comput."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b482","article-title":"Efficient construction method for phase diagrams using uncertainty sampling","volume":"3","author":"Terayama","year":"2019","journal-title":"Phys. Rev. Mater."},{"key":"10.1016\/j.compchemeng.2025.109266_b483","first-page":"37401","article-title":"Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces","volume":"35","author":"Thebelt","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b484","doi-asserted-by":"crossref","first-page":"5829","DOI":"10.1021\/acscatal.0c05661","article-title":"Catalytic methylation of m-xylene, toluene, and benzene using CO2 and H2 over TiO2-supported Re and zeolite catalysts: machine-learning-assisted catalyst optimization","volume":"11","author":"Ting","year":"2021","journal-title":"ACS Catal."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b485","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1039\/D2DD00146B","article-title":"Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS","volume":"2","author":"Tom","year":"2023","journal-title":"Digit. Discov."},{"issue":"16","key":"10.1016\/j.compchemeng.2025.109266_b486","doi-asserted-by":"crossref","first-page":"9633","DOI":"10.1021\/acs.chemrev.4c00055","article-title":"Self-driving laboratories for chemistry and materials science","volume":"124","author":"Tom","year":"2024","journal-title":"Chem. Rev."},{"key":"10.1016\/j.compchemeng.2025.109266_b487","series-title":"International Design Engineering Technical Conferences and Computers and Information in Engineering Conference","article-title":"srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications","volume":"vol. 83983","author":"Tran","year":"2020"},{"issue":"7","key":"10.1016\/j.compchemeng.2025.109266_b488","doi-asserted-by":"crossref","DOI":"10.1063\/5.0015672","article-title":"Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys","volume":"153","author":"Tran","year":"2020","journal-title":"J. Chem. Phys."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b489","doi-asserted-by":"crossref","DOI":"10.1115\/1.4046697","article-title":"sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications","volume":"20","author":"Tran","year":"2020","journal-title":"J. Comput. Inf. Sci. Eng."},{"issue":"2","key":"10.1016\/j.compchemeng.2025.109266_b490","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1039\/D2SC05089G","article-title":"Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery","volume":"14","author":"Tu","year":"2023","journal-title":"Chem. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b491","series-title":"PySCIPOpt-ML: Embedding trained machine learning models into mixed-integer programs","author":"Turner","year":"2023"},{"key":"10.1016\/j.compchemeng.2025.109266_b492","series-title":"Big Kahuna\u2013the ultimate configurable automation solution","author":"Unchainedlabs","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b493","series-title":"Reaction optimization\u2014unchainedlabs","author":"Unchainedlabs","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b494","series-title":"Vaisala","author":"Vaisala","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b495","doi-asserted-by":"crossref","DOI":"10.1016\/j.ces.2021.117135","article-title":"Data-driven optimization for process systems engineering applications","volume":"248","author":"Van De Berg","year":"2022","journal-title":"Chem. Eng. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b496","series-title":"Autoprotocol","author":"Vanessa Biggers","year":"2012"},{"key":"10.1016\/j.compchemeng.2025.109266_b497","article-title":"A chemist\u2019s guide to multi-objective optimization solvers for reaction optimization","author":"Vel","year":"2024","journal-title":"React. Chem. Eng."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b498","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0098-1354(02)00160-6","article-title":"A review of process fault detection and diagnosis: Part I: Quantitative model-based methods","volume":"27","author":"Venkatasubramanian","year":"2003","journal-title":"Comput. Chem. Eng."},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b499","doi-asserted-by":"crossref","first-page":"123","DOI":"10.2514\/1.17873","article-title":"Parallel particle swarm optimization algorithm accelerated by asynchronous evaluations","volume":"3","author":"Venter","year":"2006","journal-title":"J. Aerosp. Comput. Inf. Commun."},{"issue":"12","key":"10.1016\/j.compchemeng.2025.109266_b500","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3677119","article-title":"Counterfactual explanations and algorithmic recourses for machine learning: A review","volume":"56","author":"Verma","year":"2024","journal-title":"ACM Comput. Surv."},{"issue":"3\u20134","key":"10.1016\/j.compchemeng.2025.109266_b501","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1002\/ansa.202000155","article-title":"Recent advances in analytical techniques for high throughput experimentation","volume":"2","author":"Vervoort","year":"2021","journal-title":"Anal. Sci. Adv."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b502","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1039\/D3DD00142C","article-title":"Towards a modular architecture for science factories","volume":"2","author":"Vescovi","year":"2023","journal-title":"Digit. Discov."},{"key":"10.1016\/j.compchemeng.2025.109266_b503","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.inffus.2021.05.009","article-title":"Notions of explainability and evaluation approaches for explainable artificial intelligence","volume":"76","author":"Vilone","year":"2021","journal-title":"Inf. Fusion"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b504","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1038\/s41467-023-37139-y","article-title":"AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning","volume":"14","author":"Volk","year":"2023","journal-title":"Nat. Commun."},{"issue":"8","key":"10.1016\/j.compchemeng.2025.109266_b505","doi-asserted-by":"crossref","DOI":"10.1063\/5.0196806","article-title":"TALOS (total automation of labview operations for science): A framework for autonomous control systems for complex experiments","volume":"95","author":"Volponi","year":"2024","journal-title":"Rev. Sci. Instrum."},{"issue":"8","key":"10.1016\/j.compchemeng.2025.109266_b506","doi-asserted-by":"crossref","first-page":"3046","DOI":"10.1021\/acs.chemmater.2c03593","article-title":"Self-driving laboratory for polymer electronics","volume":"35","author":"Vriza","year":"2023","journal-title":"Chem. Mater."},{"key":"10.1016\/j.compchemeng.2025.109266_b507","doi-asserted-by":"crossref","first-page":"16422","DOI":"10.1007\/s10853-021-06281-7","article-title":"The evolution of materials acceleration platforms: toward the laboratory of the future with AMANDA","volume":"56","author":"Wagner","year":"2021","journal-title":"J. Mater. Sci."},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b508","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1039\/C8RE00345A","article-title":"An autonomous microreactor platform for the rapid identification of kinetic models","volume":"4","author":"Waldron","year":"2019","journal-title":"React. Chem. Eng."},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b509","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1039\/C7RE00123A","article-title":"Tuning reaction products by constrained optimisation","volume":"2","author":"Walker","year":"2017","journal-title":"React. Chem. Eng."},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b510","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1039\/D3DD00109A","article-title":"Go with the flow: deep learning methods for autonomous viscosity estimations","volume":"2","author":"Walker","year":"2023","journal-title":"Digit. Discov."},{"issue":"11","key":"10.1016\/j.compchemeng.2025.109266_b511","doi-asserted-by":"crossref","first-page":"5312","DOI":"10.1021\/acs.jcim.1c00637","article-title":"Nextorch: a design and Bayesian optimization toolkit for chemical sciences and engineering","volume":"61","author":"Wang","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compchemeng.2025.109266_b512","series-title":"Parallel Bayesian global optimization of expensive functions","author":"Wang","year":"2016"},{"key":"10.1016\/j.compchemeng.2025.109266_b513","series-title":"Atomic-scale effect of 2D \u03c0-conjugated metal-organic frameworks as electrocatalysts for CO2 reduction reaction towards highly selective products","author":"Wang","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b514","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1613\/jair.4806","article-title":"Bayesian optimization in a billion dimensions via random embeddings","volume":"55","author":"Wang","year":"2016","journal-title":"J. Artificial Intelligence Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b515","doi-asserted-by":"crossref","DOI":"10.3389\/fhpcp.2025.1536471","article-title":"End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment","volume":"3","author":"Wang","year":"2025","journal-title":"Front. High Perform. Comput."},{"issue":"8","key":"10.1016\/j.compchemeng.2025.109266_b516","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1038\/s42256-023-00691-9","article-title":"Self-play reinforcement learning guides protein engineering","volume":"5","author":"Wang","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.compchemeng.2025.109266_b517","doi-asserted-by":"crossref","first-page":"43645","DOI":"10.1109\/ACCESS.2024.3380842","article-title":"Research on fault diagnosis of robot arm with dynamic simulation and domain adaptation","volume":"12","author":"Wang","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.compchemeng.2025.109266_b518","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"8","key":"10.1016\/j.compchemeng.2025.109266_b519","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.1021\/acs.jctc.2c01235","article-title":"A perspective on explanations of molecular prediction models","volume":"19","author":"Wellawatte","year":"2023","journal-title":"J. Chem. Theory Comput."},{"issue":"13","key":"10.1016\/j.compchemeng.2025.109266_b520","doi-asserted-by":"crossref","first-page":"3697","DOI":"10.1039\/D1SC05259D","article-title":"Model agnostic generation of counterfactual explanations for molecules","volume":"13","author":"Wellawatte","year":"2022","journal-title":"Chem. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b521","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1016\/j.enconman.2017.03.048","article-title":"Industrial waste-heat recovery through integrated computer-aided working-fluid and ORC system optimisation using SAFT-\u03b3 Mie","volume":"150","author":"White","year":"2017","journal-title":"Energy Convers. Manage."},{"issue":"4","key":"10.1016\/j.compchemeng.2025.109266_b522","doi-asserted-by":"crossref","DOI":"10.1016\/j.device.2023.100111","article-title":"PyLabRobot: An open-source, hardware-agnostic interface for liquid-handling robots and accessories","volume":"1","author":"Wierenga","year":"2023","journal-title":"Device"},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b523","doi-asserted-by":"crossref","first-page":"3790","DOI":"10.1021\/acs.jcim.4c00292","article-title":"ORDerly: Data sets and benchmarks for chemical reaction data","volume":"64","author":"Wigh","year":"2024","journal-title":"J. Chem. Inf. Model."},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b524","article-title":"A review of molecular representation in the age of machine learning","volume":"12","author":"Wigh","year":"2022","journal-title":"Wiley Interdiscip. Rev.: Comput. Mol. Sci."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b525","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2016.18","article-title":"The FAIR guiding principles for scientific data management and stewardship","volume":"3","author":"Wilkinson","year":"2016","journal-title":"Sci. Data"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b526","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.slast.2021.11.003","article-title":"Towards robotic laboratory automation plug & play: The \u201cLAPP\u201d framework","volume":"27","author":"Wolf","year":"2022","journal-title":"SLAS Technol."},{"issue":"24","key":"10.1016\/j.compchemeng.2025.109266_b527","doi-asserted-by":"crossref","first-page":"7617","DOI":"10.1021\/acs.jcim.3c01642","article-title":"From black boxes to actionable insights: a perspective on explainable artificial intelligence for scientific discovery","volume":"63","author":"Wu","year":"2023","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b528","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1038\/s41467-025-56788-9","article-title":"Self-driving lab for the photochemical synthesis of plasmonic nanoparticles with targeted structural and optical properties","volume":"16","author":"Wu","year":"2025","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compchemeng.2025.109266_b529","series-title":"Uncertainty in Artificial Intelligence","first-page":"788","article-title":"Practical multi-fidelity Bayesian optimization for hyperparameter tuning","author":"Wu","year":"2020"},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b530","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.1016\/j.apsb.2022.11.010","article-title":"MF-SuP-pKa: Multi-fidelity modeling with subgraph pooling mechanism for pka prediction","volume":"13","author":"Wu","year":"2023","journal-title":"Acta Pharm. Sin. B"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b531","article-title":"Network attacks detection methods based on deep learning techniques: a survey","volume":"2020","author":"Wu","year":"2020","journal-title":"Secur. Commun. Netw."},{"key":"10.1016\/j.compchemeng.2025.109266_b532","first-page":"1","article-title":"Intelligent fault diagnosis for bearings of industrial robot joints under varying working conditions based on deep adversarial domain adaptation","volume":"71","author":"Xia","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"5","key":"10.1016\/j.compchemeng.2025.109266_b533","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1021\/ie050583r","article-title":"Statistical monitoring of dynamic multivariate processes part 1. Modeling autocorrelation and cross-correlation","volume":"45","author":"Xie","year":"2006","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b534","doi-asserted-by":"crossref","DOI":"10.1016\/j.pmatsci.2022.101043","article-title":"Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation","volume":"132","author":"Xie","year":"2023","journal-title":"Prog. Mater. Sci."},{"issue":"45","key":"10.1016\/j.compchemeng.2025.109266_b535","doi-asserted-by":"crossref","first-page":"53485","DOI":"10.1021\/acsami.1c16506","article-title":"Accelerate synthesis of metal\u2013organic frameworks by a robotic platform and Bayesian optimization","volume":"13","author":"Xie","year":"2021","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"10.1016\/j.compchemeng.2025.109266_b536","series-title":"BoGrape: Bayesian optimization over graphs with shortest-path encoded","author":"Xie","year":"2025"},{"key":"10.1016\/j.compchemeng.2025.109266_b537","series-title":"Xiltrix","author":"XiltriX","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b538","doi-asserted-by":"crossref","DOI":"10.1016\/j.cor.2022.105731","article-title":"A survey of job shop scheduling problem: The types and models","volume":"142","author":"Xiong","year":"2022","journal-title":"Comput. Oper. Res."},{"issue":"32","key":"10.1016\/j.compchemeng.2025.109266_b539","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6528\/acd25a","article-title":"Autonomous x-ray scattering","volume":"34","author":"Yager","year":"2023","journal-title":"Nanotechnology"},{"issue":"33","key":"10.1016\/j.compchemeng.2025.109266_b540","doi-asserted-by":"crossref","first-page":"14727","DOI":"10.1021\/acs.iecr.4c01708","article-title":"Interpretable machine learning for accelerating reverse design and optimizing CO2 methanation catalysts with high activity at low temperatures","volume":"63","author":"Yang","year":"2024","journal-title":"Ind. Eng. Chem. Res."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b541","doi-asserted-by":"crossref","DOI":"10.1063\/5.0182543","article-title":"Discovering novel halide perovskite alloys using multi-fidelity machine learning and genetic algorithm","volume":"160","author":"Yang","year":"2024","journal-title":"J. Chem. Phys."},{"key":"10.1016\/j.compchemeng.2025.109266_b542","doi-asserted-by":"crossref","DOI":"10.1002\/adfm.202470147","article-title":"Bespoke metal nanoparticle synthesis at room temperature and discovery of chemical knowledge on nanoparticle growth via autonomous experimentations","author":"Yoo","year":"2024","journal-title":"Adv. Funct. Mater."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b543","doi-asserted-by":"crossref","first-page":"9669","DOI":"10.1038\/s41467-024-54067-7","article-title":"OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler","volume":"15","author":"Yoo","year":"2024","journal-title":"Nat. Commun."},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b544","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1039\/D3DD00115F","article-title":"Digital pipette: open hardware for liquid transfer in self-driving laboratories","volume":"2","author":"Yoshikawa","year":"2023","journal-title":"Digit. Discov."},{"issue":"8","key":"10.1016\/j.compchemeng.2025.109266_b545","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1007\/s10514-023-10136-2","article-title":"Large language models for chemistry robotics","volume":"47","author":"Yoshikawa","year":"2023","journal-title":"Auton. Robots"},{"key":"10.1016\/j.compchemeng.2025.109266_b546","doi-asserted-by":"crossref","DOI":"10.1039\/D4DD00190G","article-title":"Autonomous robotic experimentation system for powder X-ray diffraction","author":"Yotsumoto","year":"2024","journal-title":"Digit. Discov."},{"issue":"23","key":"10.1016\/j.compchemeng.2025.109266_b547","doi-asserted-by":"crossref","first-page":"8995","DOI":"10.1021\/acs.iecr.3c01565","article-title":"Outlook: How I learned to love machine learning (a personal perspective on machine learning in process systems engineering)","volume":"62","author":"Zavala","year":"2023","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b548","series-title":"Zenodo","author":"Zenodo","year":"2024"},{"issue":"6","key":"10.1016\/j.compchemeng.2025.109266_b549","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1109\/TEVC.2017.2697503","article-title":"Expected improvement matrix-based infill criteria for expensive multiobjective optimization","volume":"21","author":"Zhan","year":"2017","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b550","doi-asserted-by":"crossref","first-page":"4924","DOI":"10.1038\/s41598-020-60652-9","article-title":"Bayesian optimization for materials design with mixed quantitative and qualitative variables","volume":"10","author":"Zhang","year":"2020","journal-title":"Sci. Rep."},{"issue":"1\u20132","key":"10.1016\/j.compchemeng.2025.109266_b551","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3233\/JNR-180083","article-title":"Sample environment at the China spallation neutron source","volume":"21","author":"Zhang","year":"2019","journal-title":"J. Neutron Res."},{"key":"10.1016\/j.compchemeng.2025.109266_b552","series-title":"Materials Characterization Techniques","author":"Zhang","year":"2008"},{"key":"10.1016\/j.compchemeng.2025.109266_b553","series-title":"Safe unlearning: A surprisingly effective and generalizable solution to defend against jailbreak attacks","author":"Zhang","year":"2024"},{"key":"10.1016\/j.compchemeng.2025.109266_b554","series-title":"2020 57th ACM\/IEEE Design Automation Conference","first-page":"1","article-title":"An efficient asynchronous batch Bayesian optimization approach for analog circuit synthesis","author":"Zhang","year":"2020"},{"issue":"234111","key":"10.1016\/j.compchemeng.2025.109266_b555","first-page":"10","article-title":"Battery safety: Fault diagnosis from laboratory to real world","volume":"598","author":"Zhao","year":"2024","journal-title":"J. Power Sources"},{"key":"10.1016\/j.compchemeng.2025.109266_b556","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2020.127998","article-title":"Molecular image-convolutional neural network (CNN) assisted QSAR models for predicting contaminant reactivity toward OH radicals: Transfer learning, data augmentation and model interpretation","volume":"408","author":"Zhong","year":"2021","journal-title":"Chem. Eng. J."},{"key":"10.1016\/j.compchemeng.2025.109266_b557","series-title":"A multi-robot-multi-task scheduling system for autonomous chemistry laboratories","author":"Zhou","year":"2024"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b558","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1002\/aic.14630","article-title":"Integrated solvent and process design exemplified for a Diels\u2013Alder reaction","volume":"61","author":"Zhou","year":"2015","journal-title":"AIChE J."},{"key":"10.1016\/j.compchemeng.2025.109266_b559","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"},{"key":"10.1016\/j.compchemeng.2025.109266_b560","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.ces.2016.03.011","article-title":"A hybrid stochastic\u2013deterministic optimization approach for integrated solvent and process design","volume":"159","author":"Zhou","year":"2017","journal-title":"Chem. Eng. Sci."},{"key":"10.1016\/j.compchemeng.2025.109266_b561","series-title":"Global and preference-based optimization with mixed variables using piecewise affine surrogates","author":"Zhu","year":"2023"},{"issue":"3","key":"10.1016\/j.compchemeng.2025.109266_b562","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1038\/s44160-023-00424-1","article-title":"Automated synthesis of oxygen-producing catalysts from martian meteorites by a robotic AI chemist","volume":"3","author":"Zhu","year":"2024","journal-title":"Nat. Synth."},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b563","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.1016\/S1872-2067(20)63754-8","article-title":"Tuning the intermediate reaction barriers by a CuPd catalyst to improve the selectivity of CO2 electroreduction to C2 products","volume":"42","author":"Zhu","year":"2021","journal-title":"Chin. J. Catal."},{"key":"10.1016\/j.compchemeng.2025.109266_b564","doi-asserted-by":"crossref","DOI":"10.1039\/D4DD00113C","article-title":"Discrete and mixed-variable experimental design with surrogate-based approach","author":"Zhu","year":"2024","journal-title":"Digit. Discov."},{"issue":"9","key":"10.1016\/j.compchemeng.2025.109266_b565","doi-asserted-by":"crossref","first-page":"13492","DOI":"10.1021\/acsnano.2c05303","article-title":"Bayesian active learning for scanning probe microscopy: From Gaussian processes to hypothesis learning","volume":"16","author":"Ziatdinov","year":"2022","journal-title":"ACS Nano"},{"issue":"1","key":"10.1016\/j.compchemeng.2025.109266_b566","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1038\/s41524-024-01290-x","article-title":"Growing strings in a chemical reaction space for searching retrosynthesis pathways","volume":"10","author":"Zipoli","year":"2024","journal-title":"Npj Comput. Mater."}],"container-title":["Computers &amp; Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0098135425002698?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0098135425002698?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T05:11:31Z","timestamp":1762233091000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0098135425002698"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":566,"alternative-id":["S0098135425002698"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.compchemeng.2025.109266","relation":{},"ISSN":["0098-1354"],"issn-type":[{"value":"0098-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.compchemeng.2025.109266","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109266"}}