Evgeny Podryabinkin
Hivatkozott rá
Hivatkozott rá
Active learning of linearly parametrized interatomic potentials
EV Podryabinkin, AV Shapeev
Computational Materials Science 140, 171-180, 2017
The MLIP package: moment tensor potentials with MPI and active learning
IS Novikov, K Gubaev, EV Podryabinkin, AV Shapeev
Machine Learning: Science and Technology 2 (2), 025002, 2020
Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
EV Podryabinkin, EV Tikhonov, AV Shapeev, AR Oganov
Physical Review B 99 (6), 064114, 2019
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials
K Gubaev, EV Podryabinkin, GLW Hart, AV Shapeev
Computational Materials Science 156, 148-156, 2019
Machine learning of molecular properties: Locality and active learning
K Gubaev, EV Podryabinkin, AV Shapeev
The Journal of chemical physics 148 (24), 2018
First‐principles multiscale modeling of mechanical properties in graphene/borophene heterostructures empowered by machine‐learning interatomic potentials
B Mortazavi, M Silani, EV Podryabinkin, T Rabczuk, X Zhuang, ...
Advanced Materials 33 (35), 2102807, 2021
Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials
B Mortazavi, IS Novikov, EV Podryabinkin, S Roche, T Rabczuk, ...
Applied Materials Today 20, 100685, 2020
Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
B Mortazavi, EV Podryabinkin, S Roche, T Rabczuk, X Zhuang, ...
Materials Horizons 7 (9), 2359-2367, 2020
Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution
B Mortazavi, EV Podryabinkin, IS Novikov, T Rabczuk, X Zhuang, ...
Computer Physics Communications 258, 107583, 2021
Young’s Modulus and Tensile Strength of Ti3C2 MXene Nanosheets As Revealed by In Situ TEM Probing, AFM Nanomechanical Mapping, and Theoretical …
KL Firestein, JE von Treifeldt, DG Kvashnin, JFS Fernando, C Zhang, ...
Nano Letters 20 (8), 5900-5908, 2020
Moment tensor potentials as a promising tool to study diffusion processes
II Novoselov, AV Yanilkin, AV Shapeev, EV Podryabinkin
Computational Materials Science 164, 46-56, 2019
Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials
B Mortazavi, EV Podryabinkin, IS Novikov, S Roche, T Rabczuk, ...
Journal of Physics: Materials 3 (2), 02LT02, 2020
High thermal conductivity in semiconducting Janus and non-Janus diamanes
M Raeisi, B Mortazavi, EV Podryabinkin, F Shojaei, X Zhuang, ...
Carbon 167, 51-61, 2020
Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning
Q Wang, J Ding, L Zhang, E Podryabinkin, A Shapeev, E Ma
npj Computational Materials 6 (1), 194, 2020
Moment and forces exerted on the inner cylinder in eccentric annular flow
EV Podryabinkin, VY Rudyak
Journal of Engineering Thermophysics 20 (3), 320-328, 2011
Modeling of steady Herschel–Bulkley fluid flow over a sphere
AA Gavrilov, KA Finnikov, EV Podryabinkin
Journal of Engineering Thermophysics 26, 197-215, 2017
Elinvar effect in β-Ti simulated by on-the-fly trained moment tensor potential
AV Shapeev, EV Podryabinkin, K Gubaev, F Tasnádi, IA Abrikosov
New Journal of Physics 22 (11), 113005, 2020
Detailed modeling of drilling fluid flow in a wellbore annulus while drilling
E Podryabinkin, V Rudyak, A Gavrilov, R May
International Conference on Offshore Mechanics and Arctic Engineering 55409 …, 2013
Active learning and uncertainty estimation
A Shapeev, K Gubaev, E Tsymbalov, E Podryabinkin
Machine Learning Meets Quantum Physics, 309-329, 2020
Nanohardness from First Principles with Active Learning on Atomic Environments
EV Podryabinkin, AG Kvashnin, M Asgarpour, II Maslenikov, ...
Journal of Chemical Theory and Computation 18 (2), 1109-1121, 2022
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Cikkek 1–20