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This study integrates long-range electrostatic interactions into machine-learning interatomic potentials (MLIPs). This significantly reduces energy fitting errors and improves predictions for charged organic molecules.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Machine Learning in Physics

Background:

  • Short-range machine-learning interatomic potentials (MLIPs) often struggle with accurately modeling electrostatic interactions.
  • Accurate modeling of charged molecules is crucial for understanding chemical reactions and material properties.

Purpose of the Study:

  • To enhance MLIPs by incorporating long-range electrostatic interactions using the Coulomb model with fixed charges.
  • To improve the accuracy of MLIPs for charged organic molecules and their binding properties.

Main Methods:

  • Incorporation of the Coulomb model with fixed charges into the functional form of moment tensor potentials and equivariant tensor network potentials.
  • Training and validation of the enhanced MLIPs on datasets of organic dimers of charged molecules.
  • Comparison of MLIP predictions with results from density functional theory (DFT).

Main Results:

  • Explicit inclusion of Coulomb interactions reduced energy fitting errors by over four times for short-range MLIPs.
  • The developed long-range MLIPs demonstrated significant improvements in predicting binding curves for charged organic dimers.
  • MLIP results showed good agreement with DFT calculations for the studied systems.

Conclusions:

  • Integrating long-range electrostatic interactions is a highly effective strategy for improving MLIP accuracy, especially for charged systems.
  • The enhanced MLIPs provide a computationally efficient and accurate alternative to DFT for studying charged organic molecules.
  • This work paves the way for more reliable molecular simulations involving charged species.