Active learning and explicit electrostatics enable accurate modeling of electrolytes

Abstract: Machine learning interatomic potentials (MLIPs) offer near-ab initio accuracy with the efficiency of classical force fields, making them attractive for modeling electrolytes. Collecting a diverse training set is essential for their accuracy and reliability, and explicit treatment of strong electrostatic interactions may be necessary. In this work, we demonstrated that active learning can automatically generate diverse training sets for moment tensor potentials (MTPs), enabling reliable molecular dynamics simulations of pure ethylene carbonate (EC), ethyl methyl carbonate (EMC), their mixtures, and LiPF6 solutions. The resulting MTPs exhibit excellent transferability across various EC/EMC compositions, producing ionic conductivities within 11% mean deviations from experiments. In addition, we assessed the impact of explicitly incorporating electrostatics by extending MTP with a charge redistribution scheme. Our results show that this extended MTP achieves accuracy comparable to MTP for EC/EMC mixtures with fewer parameters and reproduces ionic conductivity with only a 6% mean deviation from experiment.