A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials

TL;DR

This paper introduces LES, a versatile library that enhances machine learning interatomic potentials with accurate long-range electrostatics, improving predictions for diverse chemical systems without additional electrical property training.

Contribution

The paper presents LES as a standalone, compatible library that integrates with existing MLIPs to explicitly model long-range electrostatics, demonstrating improved accuracy across various systems.

Findings

LES improves electrostatic accuracy in MLIPs

MACELES-OFF outperforms short-range models on diverse datasets

LES enables scalable, universal electrostatic MLIPs for organic systems

Abstract

Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, CACE, and CHGNet. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same dataset, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs.