An open access solid-state battery cell database to advance visualization, search, analysis, and AI data extraction

Abstract

Despite thousands of annual publications on battery devices, standard reporting practices are distinctly absent, resulting in a patchwork of reported data that is difficult to analyze. To address this, we constructed a database of research-level solid-state batteries with an ontology-informed data framework reflected in 167 columns and populated with 245 batteries, resulting in over 40,000 data points. This project’s significance is 3-fold: (1) our variables include materials, components, fabrication, and cell testing, whose combination is essential to describe and properly assess a solid-state battery. (2) Our inclusion of “not applicable” and “not reported” values enables analysis of reporting practices and insights on “missing data” in battery publications. (3) We present a structure for battery data with well-defined variables, facilitating formatted reporting. Key applications discussed include advanced search workflow employing numerical and categorical criteria for experiment design, streamlined visualization for various actors and utilizations, and a training set for artificial intelligence (AI) paper-data extraction.