Shared Experiment Aggregation and Retrieval System (SEARS) for studying doping in organic semiconductors

The Shared Experiment Aggregation and Retrieval System (SEARS) is an open-source, cloud-based
platform designed to streamline the storage, sharing, and retrieval of experimental data in materials
science, with our example use cases focusing on doping of conjugated polymers. Addressing the
increasing need for integrated digital infrastructure, SEARS provides a flexible and intuitive environ-
ment that enables both materials scientists and data scientists to manage data seamlessly without
switching between tools. The platform supports customizable ontologies, automatic measurement
tracking, real-time visualization, and FAIR-compliant data downloads. These in turn enable more
efficient collaboration and reproducibility across research groups. SEARS was used to accelerate
data-driven insights into the role of processing conditions on charge transport in doped conjugated
polymers. By integrating with machine learning workflows, SEARS enabled the adaptive design of
experiments and quantitative structure-property relationship modeling, facilitating the exploration,
understanding, and discovery of new doping strategies. We provide the complete source code (under
MIT license), installation guidelines, and a demonstration of SEARS, illustrating its potential to
enhance data accessibility and accelerate innovation in organic electronics.