223 citations · 249 across the 22 of their papers we have counts for
23 papers
Thermal expansion mechanisms, heat transport, and band-gap renormalization in strongly anharmonic RbSO, RbCa(SO), and BaLu(BO) from machine-learned interatomic potentials
Ekaterina Spirande, Rustam Arabov, Sofya Demina +2
RbSO, RbCa(SO), and BaLu(BO) are structurally complex materials whose thermal expansion has been interpreted, on the basis of high-temperature x…
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…
Integrating moment tensor potentials with finite-element modeling for heat transfer prediction in FLiBe-based molten salt systems
Mikhail Polovinkin, Ksenia Abramova, Oksana Rahmanova +8
Molten fluoride salts are promising heat-transfer media for advanced molten salt reactors (MSRs), where reliable thermophysical property determination is critical for component des…
Machine-Learned Interatomic Potentials for Predicting Physicochemical Properties of Molten Metal-Salt Systems for Calcium Electrolysis
M. Polovinkin, N. Rybin, D. Maksimov +5
The design of efficient electrolysis devices for pure metal production requires accurate data on the properties of the melts used in the process. This work focuses on two key syste…
Thermal Conductivity and Temperature-Induced Band Gap Renormalization in Crystalline and Amorphous GaO
Rustam Arabov, Jiaxuan Li, Xiaotong Chen +2
The lattice thermal conductivity (LTC) and electron-phonon interactions in crystalline and amorphous gallium oxide are herein determined by coupling a machine-learned interatomic p…
Global Optimization of Atomic Clusters via Physically-Constrained Tensor Train Decomposition
Konstantin Sozykin, Nikita Rybin, Andrei Chertkov +5
The global optimization of atomic clusters represents a fundamental challenge in computational chemistry and materials science due to the exponential growth of local minima with sy…
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…
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…
Efficient Band Structure Unfolding with Atom-centered Orbitals: General Theory and Application
Jingkai Quan, Nikita Rybin, Matthias Scheffler +1
Band structure unfolding is a key technique for analyzing and simplifying the electronic band structure of large, internally distorted supercells that break the primitive cell's tr…
Path-integral molecular dynamics with actively-trained and universal machine learning force fields
A. A. Solovykh, N. E. Rybin, I. S. Novikov +1
Accounting for nuclear quantum effects (NQEs) can significantly alter material properties at finite temperatures. Atomic modeling using the path-integral molecular dynamics (PIMD)…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…
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…
Novel Strontium Carbides Under Compression
Nikita Rybin, Evgeny Moerman, Pranab Gain +2
Exploring the chemistry of materials at high pressures has lead to the discovery of previously unknown exotic compounds. Here, we systematically search for all thermodynamically st…
Polarisation, Born Effective Charges, and Topological Invariants via a Berry-Phase Approach
Christian Carbogno, Nikita Rybin, Sara Panahian Jand +4
This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of polarization, Born…
Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning
Olga Klimanova, Nikita Rybin, Alexander Shapeev
We present an algorithm for accelerating the search of molecule's adsorption site based on global optimization of surface adsorbate geometries. Our approach uses a machine-learning…
Temperature-dependent Electronic Spectral Functions from Band-Structure Unfolding
Jingkai Quan, Min-Ye Zhang, Nikita Rybin +5
The electronic band structure, describing the periodic dependence of electronic quantum states on lattice momentum in reciprocal space, is a fundamental concept in solid-state phys…
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…
Thermophysical Properties of Molten FLiNaK: a Moment Tensor Potential Approach
Rybin Nikita, Dmitrii Maksimov, Yuriy Zaikov +1
Fluoride salts demonstrate significant potential for applications in next-generation nuclear reactors, necessitating a comprehensive understanding of their thermophysical propertie…
Towards high-throughput superconductor discovery via machine learning
Stephen R. Xie, Y. Quan, Ajinkya Hire +5
Even though superconductivity has been studied intensively for more than a century, the vast majority of superconductivity research today is carried out in nearly the same manner a…
Novel Copper Fluoride Analogs of Cuprates
Nikita Rybin, Dmitry. Y. Novoselov, Dmitry M. Korotin +2
On the basis of the first-principles evolutionary crystal structure prediction of stable compounds in the Cu-F system, we predict two experimentally unknown stable phases -- Cu2F5…