mlsanityData to 14 Oct 2026

Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution

arXiv 2503.17949 ↗First posted 23 Mar 2025physics.chem-ph cond-mat.mtrl-sci cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. Message-passing graph neural networks, commonly used in these MLIPs, rely on local descriptor-based symmetry functions to model atomic interactions. However, such local descriptor-based approaches struggle with systems exhibiting long-range interactions, charge transfer, and compositional heterogeneity. In this work, we develop a new equivariant MLIP incorporating long-range Coulomb interactions through explicit treatment of electronic degrees of freedom, specifically global charge distribution within the system. This is achieved using a charge equilibration scheme based on predicted atomic electronegativities. We systematically evaluate our model across a range of benchmark periodic and non-periodic datasets, demonstrating that it outperforms both short-range equivariant and long-range invariant MLIPs in energy and force predictions. Our approach enables more accurate and efficient simulations of systems with long-range interactions and charge heterogeneity, expanding the applicability of MLIPs in computational materials science.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 11 of its 57 references that are in the map. First posted on arXiv 23 Mar 2025.

Builds on

11 of 57 references.

References · 11

  1. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 746
  2. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  3. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
  4. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 256
  5. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 104
  6. 2021Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistrycited by 30
  7. 2024Latent Ewald summation for machine learning of long-range interactionscited by 26
  8. 2023Physics-inspired Equivariant Descriptors of Non-bonded Interactionscited by 14
  9. 2018Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anodecited by 10
  10. 2024Learning charges and long-range interactions from energies and forcescited by 7
  11. 2020Machine Learning Enabled Discovery of Application Dependent Design Principles for Two-dimensional Materialscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025High-performance training and inference for deep equivariant interatomic potentialscited by 17
  2. 2025Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networkscited by 0
  3. 2025Long-range electrostatics for machine learning interatomic potentials is easier than we thoughtcited by 0
  4. 2026Universal and efficient graph neural networks with dynamic attention for machine learning interatomic potentialscited by 0