From the 1 of 4 linked papers with an AI index.
4 papers
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
The paper presents a multiscale framework that combines machine‑learning‑trained atomistic potentials with finite‑element simulations to predict heat‑transfer performance of FLiBe‑…
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…
Tuning Thermal Conductivity and Electron-Phonon Interactions in Carbon and Boron Nitride Moiré Diamanes via Twist Angle Manipulation
Rustam Arabov, Nikita Rybin, Victor Demin +4
We have investigated the effect of interlayer twist angle on lattice thermal conductivity (LTC) and band gap renormalization in boron nitride and carbon Moiré diamanes. Moment ten…
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…