The Role of Machine Learning Interatomic Potentials in Developing the Next Generation of Solid-State Batteries
Published in In preparation, 2025
A collaborative multi-institutional perspective reviewing the current state and future directions of machine learning interatomic potentials for solid-state battery research, covering model architectures, training data strategies, validation approaches, and outstanding challenges.
Recommended citation: Phuthi, M.K., Wei, G., Li, B., Majumdar, S., Kolluru, V.S.C., Kumar, N., Blau, S., Canepa, P., Chan, M.K.Y., Gomez-Bombarelli, R., Persson, K.A., Ceder, G., Ong, S.P. "The Role of Machine Learning Interatomic Potentials in Developing the Next Generation of Solid-State Batteries." In preparation.