Long-range electrostatics for machine learning interatomic potentials is easier than we thought

• URL: http://arxiv.org/abs/2512.18029v1
• Date: Fri, 19 Dec 2025 19:48:27 GMT
• Title: Long-range electrostatics for machine learning interatomic potentials is easier than we thought
• Authors: Dongjin Kim, Bingqing Cheng,
• Abstract summary: Lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials.<n>In this Perspective, we distill two design principles behind the Latent Ewald Summation framework.
• Score: 6.21264745750065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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