223 citations · 249 across the 22 of their papers we have counts for
5 papers · 1 filter
Low-rank approximation of Moment Tensor Potential enables reducing training set size without loss of accuracy
Anna Bondarenko, Nikita Rybin, Maxim Rakhuba +1
In this study, we implement a low-rank approximation of Moment Tensor Potential (MTP) based on the tensor train (TT) decomposition. The implemented tensor-factorized MTP (TFMTP) an…
Active learning and explicit electrostatics enable accurate modeling of electrolytes
Olga Chalykh, Mikhail Polovinkin, Dmitry Korogod +2
Machine learning interatomic potentials (MLIPs) offer near-\textit{ab initio} accuracy with the efficiency of classical force fields, making them attractive for modeling electrolyt…
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…
Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials
Igor Vorotnikov, Fedor Romashov, Nikita Rybin +2
Machine-learning interatomic potentials (MLIPs) have become a mainstay in computationally-guided materials science, surpassing traditional force fields due to their flexible functi…
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…