Accelerating battery research with an AI interface between FINALES and Kadi4Mat

TL;DR

This paper presents an AI-driven framework that integrates FINALES and Kadi4Mat to optimize battery formation protocols efficiently, reducing resource use and accelerating discovery through multi-objective Bayesian optimization.

Contribution

It introduces a novel interoperability framework between FINALES and Kadi4Mat, enabling distributed collaboration for multi-objective optimization in battery research.

Findings

Successfully identified Pareto-optimal solutions balancing formation time and EOL performance.

Demonstrated the effectiveness of multi-objective Bayesian optimization in materials science.

Established a transferable, scalable framework for data-driven materials optimization.

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.