Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants

Physical Review B: Condensed Matter and Materials Physics. 2026. Vol. 114. No. 2. Article 024104.

Korogod D., Shapeev A., Ivan S. Novikov

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 the local moment tensor potential (MTP) and demonstrate that they reduce the training errors of the MTP models fitted on the same training sets including the CH3⁢COO^−+4-methylphenol and CH3⁢COO^−+4-methylimidazole organic dimers (nonperiodic 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 long-range model with point charges dependent on atomic environments and conserving total charge is capable of predicting the correct value of the LO-TO splitting in the Γ-point in the isotropic NaCl. For this system, we also predict the dielectric constant from dipole moment fluctuations calculated with molecular-dynamics simulations conducted with the developed long-range model. The calculated dielectric constant is in good agreement with experiment. Finally, we demonstrate the broader applicability of the introduced approach by computing the phonon spectrum of uniaxial tetragonal PbTiO3. Although the method is formally derived for isotropic materials, we show that it is also a perspective for uniaxial materials (e.g., PbTiO3) as the spectrum obtained with our long-range interatomic potential corresponds to the one calculated with density functional theory.

Keywords: Electrostatic interactions, phonon spectra, dielectric constant (ϵr), magnetic machine-learning interatomic potentials