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A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials.

Dongjin Kim1, Xiaoyu Wang1, Santiago Vargas2

  • 1Department of Chemistry, University of California, Berkeley, California 94720, United States.

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Summary
This summary is machine-generated.

This study introduces the Latent Ewald Summation (LES) method, a new tool that adds long-range electrostatic interactions to machine learning interatomic potentials (MLIPs). LES improves accuracy for various systems, including biomolecules, by learning electrostatics from energy and force data.

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Area of Science:

  • Computational materials science
  • Machine learning
  • Quantum chemistry

Background:

  • Current machine learning interatomic potentials (MLIPs) often neglect long-range electrostatic interactions.
  • Accurate modeling of electrostatics is crucial for many chemical and physical phenomena.

Purpose of the Study:

  • To present the Latent Ewald Summation (LES) method as a standalone library for integrating long-range electrostatics into existing MLIPs.
  • To demonstrate the effectiveness of LES in improving the accuracy and capabilities of various MLIPs.

Main Methods:

  • Developed the Latent Ewald Summation (LES) method to infer electrostatic interactions, polarization, and Born effective charges (BECs) from energy and force data.
  • Integrated LES with several short-range MLIPs (MACE, NequIP, Allegro, CACE, CHGNet, UMA).
  • Trained a universal MLIP (MACELES-OFF) on a large, diverse dataset (SPICE) including organic molecules and clusters.

Main Results:

  • LES successfully captures correct electrostatics and enhances the accuracy of MLIPs across diverse systems like water, dipeptides, and surface adsorption.
  • The MACELES-OFF model, enhanced by LES, shows superior accuracy compared to its short-range counterpart (MACE-OFF).
  • MACELES-OFF reliably predicts dipoles and BECs, offering improved descriptions of bulk liquids and enabling electrostatic MLIPs for organic and biomolecular systems.

Conclusions:

  • The LES method provides an efficient way to incorporate long-range electrostatics into MLIPs without direct training on electrical properties.
  • LES enables the development of more accurate and versatile MLIPs, paving the way for electrostatic foundation models in materials science and chemistry.