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20092025
most citedMoment Tensor Potentials: a class of systematically improvable interatomic potentials

1.6k citations · 6.4k across the 65 of their papers we have counts for

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27 papers · 1 filter

cond-mat.mtrl-sci2025

Towards superior van der Waals density functionals for molecular crystals

Dmitry V. Fedorov, Nikita E. Rybin, Mikhail A. Averyanov +3

Ubiquitous van der Waals (vdW) interactions play a subtle yet crucial role in determining the precise atomic arrangements in solids, particularly in molecular crystals where these…

cond-mat.mtrl-sci2024

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…

cond-mat.mtrl-sci2024★ 9 cited

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…

cond-mat.mtrl-sci2023

Mechanical properties of single and polycrystalline solids from machine learning

Faridun N. Jalolov, Evgeny V. Podryabinkin, Artem R. Oganov +2

Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of methods and low accuracy of empirical…

cond-mat.mtrl-sci2023

A machine learning potential-based generative algorithm for on-lattice crystal structure prediction

Vadim Sotskov, Alexander V. Shapeev, Evgeny V. Podryabinkin

We propose a method for crystal structure prediction based on a new structure generation algorithm and on-lattice machine learning interatomic potentials. Our algorithm generates t…

cond-mat.mtrl-sci2023

Accurate melting point prediction through autonomous physics-informed learning

Olga Klimanova, Timofei Miryashkin, Alexander Shapeev

We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method…