Rustam Arabov, Jiaxuan Li, Xiaotong Chen, Nikita Rybin, Alexander Shapeev

The study investigates thermal conductivity and electron–phonon interactions in crystalline and amorphous Ga₂O₃ using a machine-learned interatomic potential (MTP) and first-principles calculations. The temperature dependence of the bandgap and thermal conductivity is compared between the two phases.

Dmitry Korogod, Alexander Shapeev, Ivan Novikov

The paper presents two models with explicit long-range electrostatic interactions that incorporate point charges depending on the local atomic environment. The second model also conserves the total charge of the atomic system. The proposed long-range models are combined with a local MTP and applied to organic dimers and crystalline NaCl.

Mikhail Polovinkin, Ksenia Abramova, Oksana Rahmanova, Farit Valiev, Andrey Isakov, Andrey Goryachikh, Alexander Galashev, Yurii Zaikov, Dmitrii Maksimov, Alexander Shapeev, Nikita Rybin

The paper presents an end-to-end modeling approach that combines molecular dynamics simulations using MTP machine-learned interatomic potentials with finite-element modeling.

M. Polovinkin, N. Rybin, D. Maksimov, F. Valiev, A. Khudorozhkova, M. Laptev, A. Rudenko, A. Shapeev.

This paper presents an approach to studying the properties of melts based on molecular modeling using MTP. The method is applied to two key systems for calcium production: Ca–Cu melt and CaCl₂–KCl electrolyte.

Jiaxuan Li, Nikita Rybin, Taowei Wang, Alexander Shapeev, Xiaotong Chen, Bing Liu

The article presents an approach to training machine-learned MTP potentials, which made it possible to describe the diffusion mechanism of cesium in amorphous and polycrystalline silicon carbide structures.

Konstantin Sozykin, Nikita Rybin, Andrei Chertkov, Anh-Huy Phan, Ivan Oseledets, Alexander Shapeev, Ivan Novikov, Gleb Ryzhakov

This paper presents a global optimization method based on tensor train decomposition, which makes it computationally efficient to find stable configurations for systems of up to 45 atoms.

Olga Chalykh, Mikhail Polovinkin, Dmitry Korogod, Nikita Rybin, Alexander Shapeev

This paper shows how machine-learned MTP potentials with explicit consideration of electrostatic interaction are used for modeling electrolytes.

Dmitry Korogod, Olga Chalykh, Max Hodapp, Nikita Rybin, Ivan Novikov, Alexander Shapeev

This paper shows how the inclusion of Coulomb interactions in machine-learned MTP potentials improves the accuracy of modeling organic dimers of charged molecules

Dmitry Fedorov, Nikita Rybin, Mikhail Averyanov, Alexander Shapeev, Artem Oganov, Carlo Nervi

The paper proposes an optimized method based on van der Waals functionals (DF1 and DF2) for accurate modeling of molecular crystal structures.

Ekaterina Spirande, Timofei Miryashkin, Andrei Kolmakov, Alexander Shapeev

A methodology for studying the thermodynamic properties of materials using molecular dynamics and phonon calculations has been developed.

Timofei Miryashkin, Olga Klimanova, Alexander Shapeev

The phase diagram as a function of temperature and composition for the Ti-V system was constructed using Bayesian regression and machine-learned MTP potentials.

Alexander Solovykh, Nikita Rybin, Ivan S. Novikov, Alexander Shapeev

A methodology is presented for using machine-learned MTP (Moment Tensor Potentials) to carry out molecular dynamics simulations taking into account quantum nuclei (path-integral molecular dynamics).

I. Chepkasov, A. Radina, V. Baidyshev, M. Polovinkin, N. Rybin, A. Shapeev, A. Krikorov, A. Oganov, Z.Dashevsky, D. Kvashnin and A. Kvashnin

This work shows how to dope PbTe to make it less brittle. Various machine learning potentials were used to study mechanical properties and heat transport.

I. Vorotnikov, F. Romashov, N. Rybin, M. Rakhuba, I. Novikov

This paper demonstrates the use of low-rank matrix and tensor decompositions for machine-learned MTP and ACE potentials, reducing the number of radial parameters by up to 50% without loss of accuracy in describing systems such as Mo-Nb-Ta-W, FLiNaK, and glycine molecular crystals.

R. Arabov, N. Rybin, V. Demin, M. Polovinkin, A. Kvashnin, L. Chernozatonskii, A. Shapeev

The effect of the twist angle on the electronic properties and heat transfer in Moiré lattices based on graphene and boron nitride was investigated.

Olga Chalykh, Dmitry Korogod, Ivan S. Novikov, Max Hodapp, Nikita Rybin, and Alexander Shapeev

A methodology is presented for taking into account long-range interactions in local machine-learned interatomic potentials.

Nikita Rybin, Evgeny Moerman, Pranab Gain, Artem R. Oganov, and Alexander Shapeev

Based on the evolutionary material structure search package, a study was conducted to determine stable phases in the Sr-C system at various pressures. A number of new structures were found.

Nikita Rybin, Dmitrii Maksimov, Yuriy Zaikov, Alexander Shapeev

Within the framework of molecular dynamics simulation with machine-learned interatomic potentials, the temperature dependences of density, viscosity, self-diffusion of elements, and thermal conductivity for molten NaF-LiF-KF salts were calculated. This composition is promising for new generation liquid-salt reactors.

Nikita Rybin, Ivan S. Novikov, Alexander Shapeev

The MTP (Moment Tensor Potential) machine-learned potential was used to accelerate the search for the most energetically favorable polymers of molecular crystals such as benzene and glycine. It is shown that using MTP can speed up the search many times over without loss of accuracy.

Olga Klimanova, Nikita Rybin, Alexander Shapeev

The MTP (Moment Tensor Potential) machine-learned potential was used to accelerate the search for the most energetically favorable adsorption positions of molecules on metal surfaces. It is shown that using MTP can speed up the search many times over without loss of accuracy.

Jiaxuan Li, Nikita Rybin, Taowei Wang, Alexander Shapeev, Xiaotong Chen, Bing Liu

Using molecular dynamics simulation with machine-learned interatomic potentials, the diffusion of Cs in amorphous and polycrystalline SiC was investigated.