3 papers
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2026
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…
physics.chem-ph2025
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…