Predicting doping strategies for ternary nickel-cobalt-manganese cathode materials to enhance battery performance using graph neural networks

TL;DR

This paper uses graph neural networks to predict optimal doping strategies in ternary NCM cathode materials, aiming to improve lithium-ion battery performance and guide experimental efforts.

Contribution

It introduces a data-driven model leveraging graph neural networks to identify effective doping strategies for NCM cathodes, addressing the challenge of selecting optimal doping methods.

Findings

Developed a comprehensive NCM battery database.

Created a GNN-based model for doping strategy prediction.

Provided insights into enhancing battery performance.

Abstract

The exceptional electrochemical performance of lithium-ion batteries has spurred considerable interest in advanced battery technologies, particularly those utilizing ternary nickel-cobalt-manganese (NCM) cathode materials, which are renowned for their robust electrochemical performance and structural stability. Building upon this research, investigators have explored doping additional elements into NCM cathode materials to further enhance their electrochemical performance and structural integrity. However, the multitude of doping strategies available for NCM battery systems presents a challenge in determining the most effective approach. In this study, we elucidate the potential of ternary NCM systems as cathode materials for lithium-ion batteries. We compile a comprehensive database of lithium-ion batteries employing NCM systems from various sources of prior research and develop a corresponding data-driven model utilizing graph neural networks to predict optimal doping strategies. Our aim is to provide insights into the NCM-based battery systems for both fundamental understanding and practical applications.