Deep learning of experimental electrochemistry for battery cathodes across diverse compositions

Abstract

Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. In this study, we present a machine-learning model (DRXNet) for battery informatics and demonstrate the application in the discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past five years, resulting in a dataset of more than 19,000 discharge voltage profiles on diverse chemistries spanning 14 different metal species. Learning from this extensive dataset, our DRXNet model can automatically capture critical features in the cycling curves of DRX cathodes under various conditions. Illustratively, the model gives rational predictions of the discharge capacity for diverse compositions in the Li--Mn--O--F chemical space as well as for high-entropy systems. As a universal model trained on diverse chemistries, our approach offers a data-driven solution to facilitate the rapid identification of novel cathode materials, accelerating the development of next-generation batteries for carbon neutralization.

References in corpus (8)

• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
• Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
• Hidden structural order controls Li-ion transport in cation-disordered oxides for rechargeable lithium batteries
• Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode
• A foundation model for atomistic materials chemistry
• The electronic-structure origin of cation disorder in transition-metal oxides
• An ${\ell }_0{\ell }_2$-norm regularized regression model for construction of robust cluster expansions in multicomponent systems