Bayesian optimization based on element mapping to design high-capacity NASICON-type cathode in sodium-ion battery

TL;DR

This paper introduces a novel element mapping strategy for Bayesian optimization, enabling efficient design of high-capacity sodium-ion battery cathodes by handling discrete elemental variables as continuous values, leading to successful material discovery.

Contribution

The paper presents a new element mapping method that improves Bayesian optimization for material design, specifically for sodium-ion battery cathodes, by encoding elements into continuous, chemically meaningful values.

Findings

Successfully suggested 16 optimal element compositions within 50 iterations.

Overcame categorical input limitations in Bayesian optimization for materials.

Broadened the applicability of Bayesian optimization in material discovery.

Abstract

To discover novel materials with high performance, there have been many attempts to adopt Bayesian optimization (BO) to materials science, owing to its efficiency in navigating complex and high-dimensional design spaces. However, the application of BO to material design has been suffered from handling discrete input variables, such as elements. Here, we introduce a novel element mapping strategy that encodes elemental identities into chemically meaningful continuous values, enabling to create easy-to-predict chemical spaces. We apply this new framework to design high capacity Na3V2(PO4)2F3 (NVPF) cathode materials for sodium-ion batteries, targeting that shift all working voltages into the desired operational voltage window. The proposed framework successfully suggests 16 optimal element composition within 50 iterations. Our results demonstrate the way to overcome the limitation of categorical input that will likely broaden the applicability of BO to a wider range of material discoveries.