Large-scale dynamics simulations of complex liquid electrolytes with NequIP equivariant machine learning models.

Electrolytes control efficiency, anode/cathode stability, battery power as well as safety, thus their optimization is crucial for the design of next-generation energy storage devices. In this work, we focus on ionic liquid electrolytes and demonstrate the application of state-of-the-art equivariant graph neural network models for interatomic interactions (NequIP [1]), trained on DFT energies and forces. Ionic liquid electrolytes exhibit a unique challenge due to their strong interactions and viscous dynamics. Additionally, substantially diverse inter-atomic environments are often present as a function of lithium-salt doping [2], raising the subtle question of model transferability. In summary, we examine the tradeoffs between computational speed and accuracy for large-scale ionic liquid molecular dynamics investigations with state-of-the-art machine learning models.