Enabling accurate modelling of materials for a solid electrolyte interphase in lithium-ion batteries using effective machine learning interatomic potentials

Abstract

is highly consistent with DFT calculations. Furthermore, we show that the generated training datasets can be applied to train graph-neural-network (GNN)-based interatomic potentials that can further improve accuracy. The developed machine learning workflow provides a scalable approach for SEI modelling, enabling simulations at larger time and length scales to tackle the limitations of conventional DFT methods.

Topics & Concepts