[2605.00909] Accelerating battery research with an AI interface between FINALES and Kadi4Mat
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Computer Science > Artificial Intelligence
arXiv:2605.00909 (cs)
[Submitted on 29 Apr 2026]
Title:Accelerating battery research with an AI interface between FINALES and Kadi4Mat
Authors:Giovanna Tosato (1), Leon Merker (1 and 2 and 3), Monika Vogler (3), Michael Selzer (1), Arnd Koeppe (1) ((1) Karlsruhe Institute of Technology, (2) Helmholtz Institute Ulm, (3) Technical University of Munich)
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Abstract:The time-consuming formation process critically impacts the longevity of sodium-ion coin cells and End Of Life (EOL) performance. This study aims to optimize formation protocols for duration efficiency, targeting high-performance outcomes while minimizing the number of experiments to reduce resource consumption and accelerate discovery. Specifically, we consider two potentially competing objectives: minimizing formation time and maximizing EOL performance. Beyond this application focus, we also present a methodological contribution: a framework designed to enable interoperability between the FINALES and Kadi RDM ecosystems, which we employ to tackle our optimization problem. In this setup, the FINALES framework orchestrates experiment planning and execution on the POLiS MAP, while an active-learning agent implemented within Kadi4Mat guides experiment selection, using multi-objective batched Bayesian optimization to efficiently explore the parameter space. This interoperability enhancement enables coordinated, distributed collaboration across automated systems and human-operated workflows, bridging multiple research centers. Using this approach, we iteratively explore the trade-off between formation time and EOL performance and identify candidate solutions approximating the Pareto front. The resulting workflow demonstrates the capability of interoperable infrastructures to facilitate data-driven optimization in battery research, and establishes a transferable framework applicable to diverse materials science and engineering optimization tasks.
Comments: Main manuscript: 21 pages, 9 figures. Supporting material: 3 pages, 5 figures. Submitted to "Batteries & Supercaps", currently under revision
Subjects:
Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
MSC classes: 62L05
ACM classes: H.4.m; I.2.m
Cite as: arXiv:2605.00909 [cs.AI]
(or arXiv:2605.00909v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.00909
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arXiv-issued DOI via DataCite
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From: Giovanna Tosato [view email] [v1] Wed, 29 Apr 2026 09:24:24 UTC (12,040 KB)
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