arXiv subjects

Dmitry Korogod

Publications and source records attributed to Dmitry Korogod.

4 recordsLinked to original sources

arXiv2026

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

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 CH$_3$COO$^-$+4-methylphenol and CH$_3$COO$^-$+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 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 $\Gamma$-point in the isotropic NaCl. For this system, we also predict 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 PbTiO$_3$. Although the method is formally derived for isotropic materials, we show that it is also perspective for uniaxial materials (e.g., PbTiO$_3$) as the spectrum obtained with our long-range interatomic potential corresponds to the one calculated with density functional theory.

physics.comp-ph↗

arXiv2025

Active learning and explicit electrostatics enable accurate modeling of electrolytes

Machine learning interatomic potentials (MLIPs) offer near-\textit{ab initio} accuracy with the efficiency of classical force fields, making them attractive for modeling electrolytes. Collecting a diverse training set is essential for their accuracy and reliability, and explicit treatment of strong electrostatic interactions may be necessary. In this work, we demonstrated that D-optimality-based active learning can automatically generate diverse training sets for moment tensor potentials (MTPs), enabling reliable molecular dynamics simulations of pure ethylene carbonate (EC), ethyl methyl carbonate (EMC), their mixtures, and LiPF$_6$ solutions. The resulting MTPs exhibit excellent transferability across various EC/EMC compositions, producing ionic conductivities within 11\% mean deviations from experiments. In addition, we assessed the impact of explicitly incorporating electrostatics by augmenting MTP with charge redistribution schemes using either fixed or environment-dependent charges. Our results show that the augmented MTP achieves the same or higher accuracy than standard model with fewer parameters, while environment-dependent charges further improve accuracy and the stability of simulations.

physics.chem-ph↗

arXiv2025

Incorporating Coulomb interactions with fixed charges in Moment Tensor Potentials and Equivariant Tensor Network Potentials

In this work, we incorporate long-range electrostatic interactions in the form of the Coulomb model with fixed charges into the functional form of short-range machine-learning interatomic potentials (MLIPs), particularly in the Moment Tensor Potential and Equivariant Tensor Network potential. We show that explicit incorporation of the Coulomb interactions with fixed charges leads to a significant reduction of energy fitting errors, namely, more than four times, of short-range MLIPs trained on organic dimers of charged molecules. Furthermore, with our long-range models we demonstrate a significant improvement in the prediction of the binding curves of the organic dimers of charged molecules. Finally, we show that the results calculated with MLIPs are in good correspondence with those obtained with density functional theory for organic dimers of charged molecules.

physics.chem-ph↗

arXiv2025

Moment Tensor Potential and Equivariant Tensor Network Potential with explicit dispersion interactions

In this study, we investigate the effect of incorporating explicit dispersion interactions in the functional form of machine learning interatomic potentials (MLIPs), particularly in the Moment Tensor Potential and Equivariant Tensor Network potential for accurate modeling of liquid carbon tetrachloride, methane, and toluene. We show that explicit incorporation of dispersion interactions via D2 and D3 corrections significantly improves the accuracy of MLIPs when the cutoff radius is set to a commonly used value of 5 -- 6 \r{A}. We also show that for carbon tetrachloride and methane, a substantial improvement in accuracy can be achieved by extending the cutoff radius to 7.5 \r{A}. However, for accurate modeling of toluene, explicit incorporation of dispersion remains important. Furthermore, we find that MLIPs incorporating dispersion interactions via D2 reach a close level of accuracy to those incorporating D3, and D2 is suitable for accurate modeling of the systems in the study, while being less computationally expensive. We evaluated the accuracy of MLIPs in dimer binding curves compared to ab initio data and in predicting density and radial distribution functions compared to experiments.

physics.chem-ph↗