Evgeny V. Podryabinkin

Skolkovo Institute of Science and Technology — Russia

Materials Science · Physical Sciences

15h-index2.3kcitations35works16.32yr avg
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Research Topics

Machine Learning in Materials Science(20), X-ray Diffraction in Crystallography(7), Advanced Materials Characterization Techniques(6), Fluid Dynamics and Turbulent Flows(5), Hydraulic Fracturing and Reservoir Analysis(4)

Publications35 total

Roadmap for the development of machine learning-based interatomic potentials
Modelling and Simulation in Materials Science and Engineering·2024· 35 citedOpen Access
Exhaustive search for novel multicomponent alloys with brute force and machine learning
npj Computational Materials·2024· 3 citedOpen Access
Mechanical Properties of Single and Polycrystalline Solids from Machine Learning
Advanced Theory and Simulations·2024· 11 cited
A machine-learning potential-based generative algorithm for on-lattice crystal structure prediction
Journal of materials research/Pratt's guide to venture capital sources·2023· 7 cited
Mechanical properties of single and polycrystalline solids from machine learning
arXiv (Cornell University)·2023Open Access
MLIP-3: Active learning on atomic environments with moment tensor potentials
The Journal of Chemical Physics·2023· 77 cited
A machine learning potential-based generative algorithm for on-lattice crystal structure prediction
arXiv (Cornell University)·2023Open Access
MLIP-3: Active learning on atomic environments with Moment Tensor Potentials
arXiv (Cornell University)·2023· 3 citedOpen Access
Mechanical Properties of Single and Polycrystalline Solids from Machine Learning
SSRN Electronic Journal·2023· 1 citedOpen Access
Nanohardness from First Principles with Active Learning on Atomic Environments
Journal of Chemical Theory and Computation·2022· 26 cited
First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials
Advanced Materials·2021· 323 citedOpen Access
Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning
npj Computational Materials·2020· 49 citedOpen Access
The MLIP package: moment tensor potentials with MPI and active learning
Machine Learning Science and Technology·2020· 40 citedOpen Access
Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution
Computer Physics Communications·2020· 203 citedOpen Access
Young’s Modulus and Tensile Strength of Ti<sub>3</sub>C<sub>2</sub> MXene Nanosheets As Revealed by <i>In Situ</i> TEM Probing, AFM Nanomechanical Mapping, and Theoretical Calculations
Nano Letters·2020· 194 cited
High thermal conductivity in semiconducting Janus and non-Janus diamanes
Carbon·2020· 58 citedOpen Access
Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials
Journal of Physics Materials·2020· 76 citedOpen Access
Active Learning and Uncertainty Estimation
Lecture notes in physics·2020· 19 cited
Moment tensor potentials as a promising tool to study diffusion processes
Computational Materials Science·2019· 100 cited
"Perfect crime" of machine-learning potentials: 100-fold speed-up with no detectable trace of using machine learning in the final result
Bulletin of the American Physical Society·2019
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Frequent Co-authors

Alexander V. Shapeev(26), Alexander G. Kvashnin(5), Konstantin Gubaev(5), Artem R. Oganov(4), Bohayra Mortazavi(4), Xiaoying Zhuang(4), Timon Rabczuk(3), Ivan S. Novikov(3), Gus L. W. Hart(3), V. Ya. Rudyak(3), Roland May(3), Павел Б. Сорокин(2), I.I. Novoselov(2), A. V. Yanilkin(2), Kamil Garifullin(2), Ivan S. Novikov(2), А. А. Гаврилов(2), Faridun N. Jalolov(2), Vadim Sotskov(2), Vladimir Tarasevich(2)