Incorporating explicit electrostatic interactions in machine learning potentials

Long-range interactions such as electrostatics have long been a concern in developing accurate, efficient machine learning potential energy surfaces (ML-PES). Many approaches for incorporating such interactions into the structure of an ML-PES have been proposed over the past decade; however, no approach has yet exhibited the combination of accuracy, generality, and conceptual simplicity necessary to find wide acceptance. In this work, we revisit one of the earliest and simplest approaches, namely, machine learning local parameters (charges) that are incorporated into a simple functional form [1], which has recently found success in the context of van der Waals interactions [2]. We test this approach on lithium-intercalated graphite, a model system for battery electrodes, where experimental data is widely available, and explore the impact of electrostatic interactions on both the dimensional changes and the Li filling pattern. Finally, we discuss wider implications for incorporating long-range interactions in future machine learning models.

[1] N. Artrith, T. Morawietz, and J. Behler, Physical Review B 83, 153101 (2011).

[2] H. Muhli, X. Chen, A. P. Bartók, P. Hernández-León, G. Csányi, T. Ala-Nissila, and M. A. Caro, Phys. Rev. B 104, 054106 (2021).