9 citations · 11 across the 8 of their papers we have counts for
8 papers
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
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, a…
Incorporating Coulomb interactions with fixed charges in Moment Tensor Potentials and Equivariant Tensor Network Potentials
Dmitry Korogod, Olga Chalykh, Max Hodapp +3
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 inte…
Moment Tensor Potential and Equivariant Tensor Network Potential with explicit dispersion interactions
Olga Chalykh, Dmitry Korogod, Ivan S. Novikov +3
In this study, we investigate the effect of incorporating explicit dispersion interactions in the functional form of machine learning interatomic potentials (MLIPs), particularly i…
Actively-trained magnetic Moment Tensor Potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN
Alexey S. Kotykhov, Max Hodapp, Christian Tantardini +4
We present a protocol for automated fitting of magnetic Moment Tensor Potential explicitly including magnetic moments in its functional form. For the fitting of this potential we u…
Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential
Nikita Rybin, Ivan S. Novikov, Alexander Shapeev
Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of the inorganic crystals, we present a methodology that exploits Moment T…
Interatomic Interaction Models for Magnetic Materials: Recent Advances
Tatiana S. Kostiuchenko, Alexander V. Shapeev, Ivan S. Novikov
Atomistic modeling is a widely employed theoretical method of computational materials science. It has found particular utility in the study of magnetic materials. Initially, magnet…
Fitting to magnetic forces improves the reliability of magnetic Moment Tensor Potentials
Alexey S. Kotykhov, Konstantin Gubaev, Vadim Sotskov +4
We developed a method for fitting machine-learning interatomic potentials with magnetic degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature of ou…
Quantum Modelling of Magnetism in Strongly Correlated Materials: Evaluating Constrained DFT and the Hubbard Model for Y114
Christian Tantardini, Darina Fazylbekova, Sergey Levchenko +1
Transition-metal compounds represent a fascinating playground for exploring the intricate relationship between structural distortions, electronic properties, and magnetic behaviour…