Research
Our research is published in peer-reviewed scientific journals, confirming the quality and reliability of the team's work. Some projects have been carried out using the Atomiсa digital platform.
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Our Publications
Anisotropy of spin waves in the field-polarized phase of Fe-doped MnSi
I. N. Khoroshiy, A. Podlesnyak, D. Menzel, M. C. Rahn, D. S. Inosov, A. S. Sukhanov, S. E. Nikitin
This paper presents the results of an experimental study of spin waves in Fe-doped magnet Mn0.9Fe0.1Si using elastic and inelastic neutron scattering methods. Strong anisotropy, uncharacteristic of cubic materials, is demonstrated.
This paper presents the results of an experimental study of spin waves in Fe-doped magnet Mn0.9Fe0.1Si using elastic and inelastic neutron scattering methods. Strong anisotropy, uncharacteristic of cubic materials, is demonstrated.
Machine-Learned Interatomic Potentials for Predicting Physicochemical Properties of Molten Metal–Salt Systems for Calcium Electrolysis
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.
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.
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.
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.
Igor Vorotnikov, Fedor Romashov, Nikita Rybin, Maxim Rakhuba, Ivan S. 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.
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.
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.
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.
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.
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.
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.
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).
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.
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 work shows how to use various matrix factorization methods to compress machine-learned interatomic potentials.
This work shows how to use various matrix factorization methods to compress machine-learned interatomic potentials.
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.
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.
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.
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.
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.
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.
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.
Using molecular dynamics simulation with machine-learned interatomic potentials, the diffusion of Cs in amorphous and polycrystalline SiC was investigated.
Collaborations
Science is a team sport
We conduct research in various fields of computational materials science. And we are happy to collaborate with other teams. Therefore, some of our research is carried out in collaboration with academic groups from various universities. If you have collaboration suggestions or questions related to our work, contact us.
Open Source Software
Contribute or just use. Our Open Source is for everyone.
Our research group has developed several software packages that we make available to the scientific community. These include MLIP-2, a package for generating interatomic potentials, and Sputnik, a tool for crystal structure prediction.
Interatomic potentials have already become working tools for computational scientists around the world. Among the first such potentials was the Moment Tensor Potential model proposed by Alexander Shapeev.
Sputnik (structure prediction using theoretical kristallographie) was developed by Nikita Rybin. It implements an approach based on evolutionary material structure search.
The MLIP-2 and Sputnik packages are open to users worldwide — try using them in your research.
Interatomic potentials have already become working tools for computational scientists around the world. Among the first such potentials was the Moment Tensor Potential model proposed by Alexander Shapeev.
Sputnik (structure prediction using theoretical kristallographie) was developed by Nikita Rybin. It implements an approach based on evolutionary material structure search.
The MLIP-2 and Sputnik packages are open to users worldwide — try using them in your research.
Learn more about Digital Materials
Contact us to discuss potential collaboration or to get answers to your questions.
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Digital Materials
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OGRN: 1247700424531
INN: 9724193709
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OGRN: 1247700424531
INN: 9724193709
Privacy policy
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