Distilling latent electrostatics from foundation machine learning interatomic potentials
Foundation machine learning interatomic potentials (MLIPs) have enabled atomistic simulations across broad regions of chemical and materials space, but many remain computationally expensive and lack explicit electrostatics, limiting their use for systems governed by long-range interactions and electrical response. Previously, we introduced Latent Ewald Summation (LES), which learns latent atomic charges and long-range electrostatics from density functional theory (DFT) energy and force labels alone. Here, we use LES to extract electrostatics that are latent in foundation models: energies and forces predicted by a teacher model are used to train a lightweight LES-augmented student MLIP, wi...
| Authors | Xiaoyu Wang, Bingqing Cheng |
|---|---|
| Primary category | physics.comp-ph |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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