7 citations · 8 across the 10 of their papers we have counts for
11 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…
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…
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…
Clarifying the Ti-V Phase Diagram Using First-Principles Calculations and Bayesian Learning
Timofei Miryashkin, Olga Klimanova, Alexander Shapeev
Conflicting experiments disagree on whether the titanium-vanadium (Ti-V) binary alloy exhibits a body-centred cubic (BCC) miscibility gap or remains completely soluble. A leading h…
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…
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…
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…
Bayesian inference of composition-dependent phase diagrams
Timofei Miryashkin, Olga Klimanova, Vladimir Ladygin +1
Phase diagrams serve as a highly informative tool for materials design, encapsulating information about the phases that a material can manifest under specific conditions. In this w…