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Moment tensor potential and equivariant tensor network potential with explicit dispersion interactions.

Olga Chalykh1, Dmitry Korogod1,2, Ivan S Novikov1,2,3,4

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Explicit dispersion interactions (D2/D3 corrections) significantly enhance machine learning interatomic potentials (MLIPs) for modeling liquids. D2 corrections offer comparable accuracy to D3 at lower computational cost.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Machine Learning

Background:

  • Machine learning interatomic potentials (MLIPs) are crucial for simulating molecular systems.
  • Accurate modeling requires capturing various interatomic forces, including dispersion interactions.

Purpose of the Study:

  • To evaluate the impact of explicit dispersion corrections (D2, D3) on MLIP accuracy.
  • To assess MLIP performance for liquid carbon tetrachloride, methane, and toluene.
  • To compare the efficacy of D2 and D3 corrections and varying cutoff radii.

Main Methods:

  • Incorporation of D2 and D3 dispersion corrections into Moment Tensor Potentials and Equivariant Tensor Network Potentials.
  • Benchmarking MLIP accuracy against ab initio dimer binding curves.
  • Validation using experimental density and radial distribution functions.

Main Results:

  • Explicit dispersion corrections significantly improve MLIP accuracy, especially at standard cutoff radii (5-6 Å).
  • Extending the cutoff radius to 7.5 Å further enhances accuracy for carbon tetrachloride and methane.
  • D2 corrections provide accuracy comparable to D3 corrections but with reduced computational expense.
  • Accurate modeling of toluene necessitates explicit dispersion incorporation.

Conclusions:

  • Explicit dispersion interactions are vital for accurate MLIPs of molecular liquids.
  • D2 corrections present a computationally efficient alternative to D3 for achieving high accuracy.
  • MLIPs with dispersion corrections show excellent agreement with both theoretical and experimental data.