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A physics-informed long-range polarizable potential based on deep learning.

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This summary is machine-generated.

This study introduces a physics-informed machine-learning potential that accurately models long-range electrostatic interactions in polar materials. The new method improves simulations of systems like water and perovskites by capturing crucial polarization effects.

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Area of Science:

  • Computational Materials Science
  • Condensed Matter Physics
  • Artificial Intelligence in Chemistry

Background:

  • Machine-learning interatomic potentials are crucial for atomistic simulations.
  • Existing models struggle with long-range electrostatic correlations in polar and biomolecular systems.

Purpose of the Study:

  • To develop a physics-informed machine-learning interatomic potential that accurately captures long-range electrostatic interactions.
  • To enhance the modeling of polar and biomolecular systems using atomistic simulations.

Main Methods:

  • Combined two equivariant message-passing neural networks for short-range and dipole interactions.
  • Incorporated a polarizable framework to model long-range electrostatics.
  • Trained the model on energies, forces, and Born effective-charge tensors.

Main Results:

  • Demonstrated improved modeling of long-range polarization effects in ionic solids, liquid water, and halide perovskites.
  • Achieved competitive accuracy in energy and force predictions.
  • Enabled accurate predictions of field-induced properties like infrared absorption spectra.

Conclusions:

  • Explicitly modeling long-range electrostatics is essential for accurate simulations of insulating and polar materials.
  • The developed potential offers a significant advancement for atomistic simulations of systems with strong polarization effects.