Charge-dependent atomic cluster expansions

Machine-learned interatomic potentials (ML-IAPs) are valuable tools for the computational modeling of materials and chemical systems. In many cases they offer highly accurate alternatives to many empirical interatomic potentials. Many of the ML-IAPs rely on the use of descriptors that encode the local chemical bonding environment about a central atom. This local description of atomic interactions restricts many ML-IAPs to systems where long-range effects are negligible, where long-range effects are sufficiently screened, or where only small amounts of charge transfer occur. Long-range coulomb interactions and charge transfer are not insignificant when considering phenomena such as corrosion, oxidation, and related atomic systems. Many recent advances in ML-IAPs, such as message-passing networks and charge-dependent descriptors, help alleviate these deficiencies, however these new developments have their own limitations. Their utility is often limited by the methods for determining atomic charges. Like empirical potentials, methods for charge relaxation are often inaccurate and/or expensive. Using newly developed charge-dependent atomic cluster expansion (ACE) descriptors and shadow molecular dynamics-inspired charge relaxation schemes, we help address some of these challenges with charge-dependent ML-IAPs. In this work, we explore the benefits of using charge-dependent ACE ML-IAPs and how they can help correct spurious behavior typically encountered in dynamic-charge molecular dynamics simulations.