Data-Driven Understand of High Entropy Rocksalt Type Li-Ion Battery Cathodes

High entropy battery materials (HEBMs) have rapidly emerged in recent years. They offer new pathways for achieving excellent electrochemical performance and show promise in reducing reliance on critical metals, such as Ni and Co. Specifically, high entropy disordered rocksalt type (HE-DRX) cathodes have demonstrated exceptional rate performance attributed to enhanced disorder. In this presentation, we will share a dataset comprising 18810 DFT-computed high entropy disordered rocksalt type (HE-DRX) Li-ion battery cathode materials. Our computational investigation comprehensively explores the entire combinatorial space of 27 typical metal species and various Li or F contents present in DRX cathodes. Building on our previous research, which revealed enhanced rate capability by minimizing chemical short-range order, we will present our interpretation of design principles aimed at creating more stable and more disordered HE-DRX cathodes. Eventually, we will also demonstrate our global machine-learning model that can predict the stability of any HE-DRX cathode composition with an error of less than 5 meV/atom.