14 citations · 16 across the 8 of their papers we have counts for
9 papers
Compressed magnetic Moment Tensor Potentials via low-rank matrix and tensor factorizations
Alexey S. Kotykhov, Alexander V. Shapeev, Maxim Rakhuba +1
We propose a parameter-reduced version of magnetic Moment Tensor Potential (mMTP) based on various matrix and tensor decompositions. The resulting compressed magnetic machine-learn…
HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration
Daria A. Ryabchenko, Pavel Gurevich, Shamil Kadyrov +5
Generative molecular models can support early drug discovery by proposing new candidate compounds de novo. In practice, useful candidates must balance target-relevant activity, phy…
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…
Automated Prediction of Thermodynamic Properties via Bayesian Free-Energy Reconstruction from Molecular Dynamics
Ekaterina Spirande, Timofei Miryashkin, Andrei Kolmakov +1
Accurate free-energy calculations are essential for predicting thermodynamic properties and phase stability, but existing methods are limited: phonon-based approaches neglect anhar…
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…
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…
A moment tensor potential for lattice thermal conductivity calculations of alpha and beta phases of Ga2O3
Nikita Rybin, Alexander Shapeev
Calculations of heat transport in crystalline materials have recently become mainstream, thanks to machine-learned interatomic potentials that allow for significant computational c…
Automated optimization of convergence parameters in plane wave density functional theory calculations via a tensor decomposition-based uncertainty quantification
Jan Janssen, Edgar Makarov, Tilmann Hickel +2
First principles approaches have revolutionized our ability in using computers to predict, explore and design materials. A major advantage commonly associated with these approaches…