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Learning Long-Range Interactions in Equivariant Machine Learning Interatomic Potentials via Electronic Degrees of

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This study introduces a new machine learning interatomic potential (MLIP) that accurately models long-range interactions and charge transfer in materials. The enhanced MLIP improves predictions for complex systems, expanding the scope of computational materials science.

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Area of Science:

  • Computational Materials Science
  • Machine Learning
  • Quantum Mechanics

Background:

  • Machine learning interatomic potentials (MLIPs) offer efficient alternatives to quantum mechanical simulations.
  • Current MLIPs using graph neural networks struggle with long-range interactions and charge transfer due to local descriptors.

Purpose of the Study:

  • Develop a novel equivariant MLIP that explicitly incorporates long-range Coulomb interactions.
  • Improve the accuracy and efficiency of MLIPs for systems with charge heterogeneity.

Main Methods:

  • Incorporated long-range Coulomb interactions by treating electronic degrees of freedom and global charge distribution.
  • Utilized a charge equilibration scheme based on predicted atomic electronegativities.
  • Developed a new equivariant MLIP model.

Main Results:

  • The new MLIP outperforms existing short-range and long-range MLIPs in energy and force predictions.
  • Achieved higher accuracy with a smaller cutoff radius (4 Å) compared to short-range models (6 Å).
  • Demonstrated superior performance on both periodic and nonperiodic benchmark datasets.

Conclusions:

  • The developed MLIP accurately simulates systems with long-range interactions and charge heterogeneity.
  • This advancement expands the applicability of MLIPs in computational materials science.
  • Offers a more efficient and accurate approach for materials simulations.