Authors: Karl Larson | Basila Kattouf | Delina Damatov | Assaf Y. Anderson | Paul Albertus
Source: ECS Meeting Abstracts (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Defining an Ontology for All Solid-State Battery Cells for Database Implementation New developments and publications on in alkali metal solid-state batteries appear regularly, with the number of publications now numbering in the hundreds to low thousands, but the search and analysis of the data contained in these publications is still largely done by manually reading through each article. Such challenges are also present in other fields, such as perovskite solar cells, for which successful efforts to create a database have been pursued. Review articles can provide valuable summaries and insights on groupings of these publications, but the rapid rate of development and the “static” nature of a review paper limits their usefulness. As such, a structured database is desirable to facilitate the search of public reports for specific cell conditions or cell performance, and to provide data in electronic and internationally accessible format to allow for analysis with a range of statistical and machine learning tools. However, defining an ontology that can efficiently capture the large range of materials, cell designs, processing conditions, test conditions, and results is required for a broadly useful database. Here we discuss our efforts in developing an ontology for solid-state battery cells, and preliminary results on a database we are constructing based on that ontology. Key areas for the ontology include component material identities, material properties, component properties (e.g., active material loading, mass fractions, thicknesses), processing conditions for materials and the cell itself (e.g., ball milling time, sintering time), and both performance (e.g., rate capability) and degradation (e.g., cycle life). We welcome feedback from the community interested in or engaged in similar and growing efforts to more systematically define and make available the large volumes of battery data currently being reported. (1) Ward, L.; Babinec, S.; Dufek, E. J.; Howey, D. A.; Viswanathan, V.; Aykol, M.; Beck, D. A. C.; Blaiszik, B.; Chen, B. R.; Crabtree, G.; Clark, S.; De Angelis, V.; Dechent, P.; Dubarry, M.; Eggleton, E. E.; Finegan, D. P.; Foster, I.; Gopal, C. B.; Herring, P. K.; Hu, V. W.; Paulson, N. H.; Preger, Y.; Uwe-Sauer, D.; Smith, K.; Snyder, S. W.; Sripad, S.; Tanim, T. R.; Teo, L. Principles of the Battery Data Genome. Joule 2022 , 6 (10), 2253–2271. https://doi.org/10.1016/J.JOULE.2022.08.008. (2) Jacobsson, T. J.; Hultqvist, A.; García-Fernández, A.; Anand, A.; Al-Ashouri, A.; Hagfeldt, A.; Crovetto, A.; Abate, A.; Ricciardulli, A. G.; Vijayan, A.; Kulkarni, A.; Anderson, A. Y.; Darwich, B. P.; Yang, B.; Coles, B. L.; Perini, C. A. R.; Rehermann, C.; Ramirez, D.; Fairen-Jimenez, D.; Di Girolamo, D.; Jia, D.; Avila, E.; Juarez-Perez, E. J.; Baumann, F.; Mathies, F.; González, G. S. A.; Boschloo, G.; Nasti, G.; Paramasivam, G.; Martínez-Denegri, G.; Näsström, H.; Michaels, H.; Köbler, H.; Wu, H.; Benesperi, I.; Dar, M. I.; Bayrak Pehlivan, I.; Gould, I. E.; Vagott, J. N.; Dagar, J.; Kettle, J.; Yang, J.; Li, J.; Smith, J. A.; Pascual, J.; Jerónimo-Rendón, J. J.; Montoya, J. F.; Correa-Baena, J. P.; Qiu, J.; Wang, J.; Sveinbjörnsson, K.; Hirselandt, K.; Dey, K.; Frohna, K.; Mathies, L.; Castriotta, L. A.; Aldamasy, M. H.; Vasquez-Montoya, M.; Ruiz-Preciado, M. A.; Flatken, M. A.; Khenkin, M. V.; Grischek, M.; Kedia, M.; Saliba, M.; Anaya, M.; Veldhoen, M.; Arora, N.; Shargaieva, O.; Maus, O.; Game, O. S.; Yudilevich, O.; Fassl, P.; Zhou, Q.; Betancur, R.; Munir, R.; Patidar, R.; Stranks, S. D.; Alam, S.; Kar, S.; Unold, T.; Abzieher, T.; Edvinsson, T.; David, T. W.; Paetzold, U. W.; Zia, W.; Fu, W.; Zuo, W.; Schröder, V. R. F.; Tress, W.; Zhang, X.; Chiang, Y. H.; Iqbal, Z.; Xie, Z.; Unger, E. An Open-Access Database and Analysis Tool for Perovskite Solar Cells Based on the FAIR Data Principles. Nature Energy 2021 7:1 2021 , 7 (1), 107–115. https://doi.org/10.1038/s41560-021-00941-3. Computer science ; Database ; Ontology ; State (computer science) ; Battery (electricity) ; Solid-state ; Information retrieval ; Programming language ; Engineering ; Physics ; Engineering physics Machine Learning in Materials Science ; Graph Theory and Algorithms