Alexander Shapeev
Alexander Shapeev
Skolkovo Institute of Science and Technology
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
Moment tensor potentials: A class of systematically improvable interatomic potentials
AV Shapeev
Multiscale Modeling & Simulation 14 (3), 1153-1173, 2016
Active learning of linearly parametrized interatomic potentials
EV Podryabinkin, AV Shapeev
Computational Materials Science 140, 171-180, 2017
Performance and cost assessment of machine learning interatomic potentials
Y Zuo, C Chen, X Li, Z Deng, Y Chen, J Behler, G Csányi, AV Shapeev, ...
The Journal of Physical Chemistry A 124 (4), 731-745, 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
Consistent energy-based atomistic/continuum coupling for two-body potentials in one and two dimensions
AV Shapeev
Multiscale Modeling & Simulation 9 (3), 905-932, 2011
Machine learning of molecular properties: Locality and active learning
K Gubaev, EV Podryabinkin, AV Shapeev
The Journal of chemical physics 148 (24), 241727, 2018
Analysis of boundary conditions for crystal defect atomistic simulations
V Ehrlacher, C Ortner, AV Shapeev
Archive for Rational Mechanics and Analysis 222 (3), 1217-1268, 2016
Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials
T Kostiuchenko, F Körmann, J Neugebauer, A Shapeev
npj Computational Materials 5 (1), 1-7, 2019
Analysis of an energy-based atomistic/continuum approximation of a vacancy in the 2D triangular lattice
C Ortner, A Shapeev
Mathematics of Computation 82 (284), 2191-2236, 2013
Machine-learned multi-system surrogate models for materials prediction
C Nyshadham, M Rupp, B Bekker, AV Shapeev, T Mueller, ...
npj Computational Materials 5 (1), 1-6, 2019
Deep elastic strain engineering of bandgap through machine learning
Z Shi, E Tsymbalov, M Dao, S Suresh, A Shapeev, J Li
Proceedings of the National Academy of Sciences 116 (10), 4117-4122, 2019
An asymptotic fitting finite element method with exponential mesh refinement for accurate computation of corner eddies in viscous flows
AV Shapeev, P Lin
SIAM Journal on Scientific Computing 31 (3), 1874-1900, 2009
Theory-based benchmarking of the blended force-based quasicontinuum method
XH Li, M Luskin, C Ortner, AV Shapeev
Computer methods in applied mechanics and engineering 268, 763-781, 2014
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
Consistent energy-based atomistic/continuum coupling for two-body potentials in three dimensions
AV Shapeev
SIAM Journal on Scientific Computing 34 (3), B335-B360, 2012
Ab initio vibrational free energies including anharmonicity for multicomponent alloys
B Grabowski, Y Ikeda, P Srinivasan, F Körmann, C Freysoldt, AI Duff, ...
npj Computational Materials 5 (1), 1-6, 2019
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
An optimization-based atomistic-to-continuum coupling method
D Olson, PB Bochev, M Luskin, AV Shapeev
SIAM Journal on Numerical Analysis 52 (4), 2183-2204, 2014
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
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