SEARS: a lightweight FAIR platform for multi-lab materials experiments and closed-loop optimization

Abstract

The Shared Experiment Aggregation and Retrieval System (SEARS) is an open-source, lightweight, cloud-native platform that captures, versions, and exposes materials-experiment data and metadata via FAIR, programmatic interfaces. Designed for distributed, multi-lab workflows, SEARS provides configurable, ontology-driven data-entry screens backed by a public definitions registry (terms, units, provenance, versioning); automatic measurement capture and immutable audit trails; storage of arbitrary file types with JSON sidecars; real-time visualization for tabular data; and a documented REST API and Python SDK for closed-loop analysis (e.g., adaptive design of experiments) and model building (e.g., QSPR). We illustrate SEARS on doping studies of the high mobility conjugated polymer, pBTTT, with the dopant, F4TCNQ, where experimental and data-science teams iterated across sites using the API to propose and execute new processing conditions, enabling efficient exploration of ternary co-solvent composition and annealing temperature effects on sheet resistance of doped pBTTT films. SEARS does not claim novelty in these scientific methods; rather, it operationalizes them with rigorous provenance and interoperability, reducing handoff friction and improving reproducibility. Source code (MIT license), installation scripts, and a demonstration instance are provided. By making data findable, accessible, interoperable, and reusable across teams, SEARS lowers the barrier to collaborative materials research and accelerates the path from experiment to insight.