{"ID":2827264,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18029","arxiv_id":"2512.18029","title":"Long-range electrostatics for machine learning interatomic potentials is easier than we thought","abstract":"The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar and ionic materials, and biomolecules. In this Perspective, we distill two design principles behind the Latent Ewald Summation (LES) framework, which can capture long-range interactions, charges, and electrical response just by learning from standard energy and force training data: (i) use a Coulomb functional form with environment-dependent charges to capture electrostatic interactions, and (ii) avoid explicit training on ambiguous density functional theory (DFT) partial charges. When both principles are satisfied, substantial flexibility remains: essentially any short-range MLIP can be augmented; charge equilibration schemes can be added when desired; dipoles and Born effective charges can be inferred or finetuned; and charge/spin-state embeddings or tensorial targets can be further incorporated. We also discuss current limitations and open challenges. Together, these minimal, physics-guided design rules suggest that incorporating long-range electrostatics into MLIPs is simpler and perhaps more broadly applicable than is commonly assumed.","short_abstract":"The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar and ionic materials, and biomolecules. In this Perspective, we distill two design principles behind the Latent Ewald Summat...","url_abs":"https://arxiv.org/abs/2512.18029","url_pdf":"https://arxiv.org/pdf/2512.18029v1","authors":"[\"Dongjin Kim\",\"Bingqing Cheng\"]","published":"2025-12-19T19:48:27Z","proceeding":"physics.comp-ph","tasks":"[\"physics.comp-ph\",\"cond-mat.mtrl-sci\",\"cs.LG\",\"physics.chem-ph\"]","methods":"[]","has_code":false}