Accelerating battery research with an AI interface between FINALES and Kadi4Mat

2026-04-29

AI Summary

This work addresses the trade-off between formation time and end-of-life performance in sodium-ion coin cell formation processes by proposing an efficient research paradigm that integrates multi-objective batch Bayesian optimization with active learning. For the first time, interoperability between the FINALES and Kadi4Mat scientific data management systems is achieved and combined with the POLiS MAP automated experimentation platform, establishing an AI-driven optimization framework that supports cross-center, human–machine collaborative research. The proposed approach substantially reduces the number of required experiments while rapidly converging toward the Pareto front, thereby demonstrating the effectiveness and transferability of interoperable infrastructures in data-driven battery materials development.

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.

Problem

• formation protocol
• sodium-ion battery
• End Of Life performance
• multi-objective optimization
• battery research

Innovation

• interoperability
• multi-objective Bayesian optimization
• active learning
• battery formation
• research data management