Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants
Dmitry Korogod, Alexander V. Shapeev, Ivan S. Novikov

TL;DR
This paper introduces environment-dependent long-range electrostatic models integrated with machine learning potentials, enabling accurate predictions of phonon spectra, LO-TO splitting, and dielectric constants in various materials.
Contribution
The paper presents novel long-range electrostatic models with environment-dependent charges that improve machine learning potentials for predicting dielectric and vibrational properties.
Findings
Accurately predicts LO-TO splitting in NaCl.
Calculates dielectric constants consistent with experimental data.
Demonstrates applicability to both isotropic and uniaxial materials.
Abstract
We present two models with explicit long-range electrostatics in the form of Coulomb interactions. Both models include point charges depending on their local atomic environments, and the second model also conserves a total charge of an atomic system. We combine the proposed long-range models with local Moment Tensor Potential and demonstrate that they reduce the training errors of the MTP models fitted on the same training sets including the CHCOO+4-methylphenol and CHCOO+4-methylimidazole organic dimers (non-periodic systems) and the NaCl crystal (periodic system). For the organic dimers, the proposed models also give qualitatively correct predictions of the binding curves. Furthermore, in this study we introduce a method for calculating phonon spectra of isotropic materials only via these long-range models fitted to energies, forces, and stresses. The developed…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Physical and Chemical Molecular Interactions · Material Dynamics and Properties
