Interpretable Machine Learning Prediction of Voltage and Specific Capacity for Electrode Materials

Abstract

AbstractThe application of machine learning (ML) is becoming widespread and playing an important role in the property prediction and design of battery. Average voltage and specific capacity are two important indicators of electrode materials, which determine suitability of electrode materials in batteries. In this study, 4351 data are collected from Material Project and 324 features are extracted from Material Project or calculated by Matminer. Four ML models are discussed for prediction of average voltage and specific capacity. In order to ensure the prediction accuracy and efficiency of ML models, sequential backward selection (SBS) method is introduced to select optimal feature set, which can reduce redundancy features. The combination of Deep Neural Network model and optimal feature set selected by the SBS method achieves accurate prediction and outperforms Crystal Graph Convolutional Neural Network and Graph‐attention Graph Neural Network. The interpretability analysis provides insight into the relationship between features and two target properties. To cope with the poor prediction performance for metal‐ion battery with scarce data, transfer learning is adopted and an excellent improvement is achieved in the prediction in Na‐ion, Mg‐ion and Ca‐ion electrode material. It can be concluded that ML is an effective approach to battery property prediction and design.