Ph.D. thesis: “Machine-learning Interatomic Potentials for Multicomponent Alloys”
Supervisor: Alexander Shapeev
Konstantin graduated from Moscow Institute of Physics and Technology in 2013 (BSc, devoted to nonlinear elasticity of liquid crystals) and 2015 (MSc, devoted to numerical simulation of hydrodynamics and of heat and charge transfer during hybrid laser-arc spot welding process). His research at Skoltech is focused on developing the machine-learning interatomic potentials (MLIPs) for multicomponent systems. These types of potentials are surrogate models interpolating the quantum mechanical data (energies, forces and stresses for atomic systems). MLIPs developed by Konstantin allow performing molecular dynamics or structure relaxations orders of magnitude faster than with ab initio methods, with an accuracy comparable to the accuracy of reference data provided. The MLIPs were verified on the tasks of predicting the properties of organic molecules and were also used for calculation of elastic constants, phase diagrams and convex hulls for metallic alloys. His research interests include: phase stability in high-entropy alloys, finite-temperature properties of alloys, atomistic simulations.
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