Evgeny V. Podryabinkin

Active learning of linearly parametrized interatomic potentials

Evgeny V. Podryabinkin, Alexander V. Shapeev · Computational Materials Science · 2017 · 685 citations · Full text

Natural Sciences, Chemistry, Computational Chemistry +12

Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning

Evgeny V. Podryabinkin, Alexander V. Shapeev, Artem R. Oganov · Physical review. B./Physical review. B · 2019 · 391 citations · Full text

Natural Sciences, Chemistry, Physics +17

First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials

Bohayra Mortazavi, Mohammad Silani, Evgeny V. Podryabinkin et al. · Advanced Materials · 2021 · 333 citations · Full text

Natural Sciences, Materials Science, Physics +17

Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution

Bohayra Mortazavi, Evgeny V. Podryabinkin, Ivan S. Novikov et al. · Computer Physics Communications · 2020 · 215 citations · Full text

Natural Sciences, Chemistry, Physics +17

Young’s Modulus and Tensile Strength of Ti₃C₂ MXene Nanosheets As Revealed by <i>In Situ</i> TEM Probing, AFM Nanomechanical Mapping, and Theoretical Calculations

Konstantin L. Firestein, Joel E. von Treifeldt, Dmitry G. Kvashnin et al. · Nano Letters · 2020 · 207 citations

Natural Sciences, Materials Engineering, Materials Science +17