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

TL;DR

This paper presents a simple, physics-guided approach to incorporate long-range electrostatics into machine learning interatomic potentials, overcoming previous limitations and broadening their applicability.

Contribution

The authors introduce the Latent Ewald Summation framework with two key principles, enabling MLIPs to effectively model long-range electrostatics without explicit DFT charge training.

Findings

Long-range electrostatics can be integrated into MLIPs using simple design principles.

The framework allows augmentation of existing MLIPs with minimal modifications.

Incorporating electrostatics enhances MLIP applicability to complex materials and interfaces.

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.