Accelerating battery research with an AI interface between FINALES and Kadi4Mat

通过 FINALES 与 Kadi4Mat 之间的 AI 接口加速电池研究

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.

摘要： 耗时的化成（formation）过程对钠离子扣式电池的寿命及寿命终点（EOL）性能有着至关重要的影响。本研究旨在优化化成方案以提高时长效率，在追求高性能结果的同时，尽量减少实验次数，从而降低资源消耗并加速科学发现。

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.

具体而言，我们考虑了两个潜在的竞争目标：最小化化成时间和最大化 EOL 性能。除了这一应用重点外，我们还提出了一项方法论贡献：一个旨在实现 FINALES 和 Kadi RDM 生态系统之间互操作性的框架，并利用该框架来解决我们的优化问题。

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.

在此设置中，FINALES 框架负责协调 POLiS MAP 上的实验规划与执行，而 Kadi4Mat 中实现的主动学习代理则通过多目标批量贝叶斯优化来指导实验选择，从而高效地探索参数空间。

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.

这种互操作性的增强实现了跨自动化系统和人工操作工作流的协调分布式协作，连接了多个研究中心。通过这种方法，我们迭代地探索了化成时间与 EOL 性能之间的权衡，并确定了接近帕累托前沿（Pareto front）的候选解决方案。

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.

最终形成的工作流展示了互操作基础设施在促进电池研究中数据驱动优化方面的能力，并建立了一个可迁移的框架，适用于多种材料科学和工程优化任务。