Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution

• URL: http://arxiv.org/abs/2503.17949v1
• Date: Sun, 23 Mar 2025 05:26:55 GMT
• Title: Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
• Authors: Moin Uddin Maruf, Sungmin Kim, Zeeshan Ahmad,
• Abstract summary: Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties.<n>We develop a new equivariant MLIP incorporating long-range Coulomb interactions through explicit treatment of electronic degrees of freedom.<n>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.
• Score: 1.6112718683989882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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