Work

Latent Ewald summation for machine learning of long-range interactions

Year: 2025

Type: article

Abstract: Machine learning interatomic potentials (MLIPs) often neglect long-range interactions, such as electrostatic and dispersion forces. In this work, we introduce a straightforward and efficient method to account for long-range interactions by learning a hidden variable from local atomic descriptors and applying an Ewald summation to this variable. We demonstrate that in systems including charged and polar molecular dimers, bulk water, and water-vapor interface, standard short-ranged MLIPs can lead to unphysical predictions even when employing message passing. The long-range models effectively eliminate these artifacts, with only about twice the computational cost of short-range MLIPs.

Source: npj Computational Materials

DOI: 10.1038/s41524-025-01577-7

Author: Bingqing Cheng

Institutions: Institute of Science and Technology Austria, University of California, Berkeley

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,490

OpenAlex ID: W4408876037

FWCI: 18.81

Cites: 34

Cited by: 92

Related to: 10