ACGNet: An interpretable attention crystal graph neural network for accurate oxidation potential prediction

Abstract

LiNixCoyMn1-x-yO2 (NCM) is one of the most critical cathode materials for high energy density lithium-ion batteries in electric vehicle applications. Nevertheless, capacity degradation and long-term cycle instability due to the aging of cathode/electrolyte interfaces remain significant challenges for NCM materials. Various surface stabilization techniques, including doping and coating, can be employed for NCM modifications. Traditionally, new coating materials are identified by chemical intuition or trial-and-error synthesis, which hinders the discovery speed of high-performance coating materials. A novel neural network model named Attention Graph Convolutional Neural Network (ACGNet) has been developed to predict crystals’ electrochemical stability windows from atom and bonding features and exhibits remarkable predictive performance with a mean absolute error of 0.586 V. Then, the developed model is utilized to conduct high-throughput screening of 13,943 candidate compounds for their coating potential. Among the candidates, LiPO3 exhibits prominent potential as a coating material due to its high oxidation voltage and low preparation cost. The subsequent battery assembly experiment and electrochemical characterization reveal that the incorporation of LiPO3 significantly enhances the cycle stability of NCM batteries. In summary, our model exhibits exceptional accuracy in predicting material properties and serves as a compelling example for machine learning applications in battery materials.