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SEARS: A Lightweight FAIR Platform for Multi-Lab Materials Experiments and Closed-Loop Optimization

delete2025-10-07
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OA
AI
R
Ronak Tali
A
Ankush Kumar Mishra
D
Devesh Lohia
J
Jacob Paul Mauthe
J
Justin Neu
S
Sung-Joo Kwon
Y
Yusuf Olanrewaju
A
Aditya Balu
G
Goce Trajcevski
F
Franky So
尤
尤伟 (Wei You)
A
Aram Amassian
B
Baskar Ganapathysubramanian
DOI：10.1039/D5DD00175Gdelete
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Abstract

Abstract

En 中文
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 environment 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.

Journal

Digital Discovery cover
Digital Discovery
IF:
5.6
Papers:
971
Citations:
1.7K

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No organization information available