Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

ABSTRACT Layered oxides have caught much attention as cathode materials for Na‐ion batteries. For a rational cathode material design and performance improvement, accurate prediction of the most stable stacking sequence (e.g., P2 vs. O3 phase) of layered oxides is inevitable. Here, we first developed a data‐driven model based on machine learning (ML) to predict the phase stability of layered oxides. Afterward, with combination of electrostatic analysis, density functional theory, Monte Carlo simulation, and thermodynamics consideration, we validated our ML‐based prediction and gained insight into the interrelation between features and phase stabilities. We found that deep neural networks can predict phase stability with a very high accuracy. TM ionic potential, Na concentration, and TM mixing entropy are identified as the key factors influencing phase classification, with lower TM ionic potential, higher Na content, and higher mixing entropy favoring the O3 phase. We found that Na‐TM and Na‐Na interactions are key factors controlling the phase stability and both are strongly Na concentration dependent. Finally, the TM ionic potential is determined to be the decisive factor controlling Na‐TM interaction and thereby the phase stability of layered oxide cathode materials for Na‐ion batteries.