Alexander Shapeev

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Evgeny PodryabinkinSkolkovo Institute of Science and TechnologyZweryfikowany adres z skoltech.ru
Bohayra MortazaviLeibniz Universität HannoverZweryfikowany adres z iop.uni-hannover.de
Xiaoying ZhuangLeibniz University HanoverZweryfikowany adres z iop.uni-hannover.de
Ivan NovikovSenior Research Scientist, Skolkovo Institute of Science and TechnologyZweryfikowany adres z skoltech.ru
Timon RabczukBauhaus University WeimarZweryfikowany adres z uni-weimar.de
Jörg NeugebauerDirector, Max-Planck-Institut für EisenforschungZweryfikowany adres z mpie.de
Fritz KörmannUniversity of Stuttgart, ICAMS Bochum, MPIE DüsseldorfZweryfikowany adres z mpie.de
Evgenii TsymbalovApptekZweryfikowany adres z apptek.com
Gus L. W. HartBrigham Young University, Physics and AstronomyZweryfikowany adres z byu.edu
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeZweryfikowany adres z cam.ac.uk
Christoph OrtnerUniversity of British ColumbiaZweryfikowany adres z math.ubc.ca
Blazej GrabowskiInstitute for Materials Science, University of StuttgartZweryfikowany adres z imw.uni-stuttgart.de
Mitchell LuskinProfessor of Mathematics, University of MinnesotaZweryfikowany adres z umn.edu
Ming DaoMassachusetts Institute of TechnologyZweryfikowany adres z mit.edu
Conrad RosenbrockBrigham Young UniversityZweryfikowany adres z byu.edu
Brahmanandam JavvajiPostdoc Scientific Coworker, Chair of Computational Science and Simulation Technology, Insisute ofZweryfikowany adres z iop.uni-hannover.de
Alexander G. KvashninFull Professor, Skolkovo Institute of Science and TechnologyZweryfikowany adres z skoltech.ru
Max HodappMaterials Center Leoben Forschung GmbH (MCL)Zweryfikowany adres z mcl.at
Stephan RocheICREA Research Professor, Catalan Institute of Nanoscience and NanotechnologyZweryfikowany adres z icn2.cat
Jörg BehlerRuhr-Universität BochumZweryfikowany adres z ruhr-uni-bochum.de

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Alexander Shapeev

Skolkovo Institute of Science and Technology

Zweryfikowany adres z shapeev.com - Strona główna

Applied Mathematics Numerical Analysis Scientific Computing Materials Science


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
Moment tensor potentials: A class of systematically improvable interatomic potentials AV Shapeev Multiscale Modeling & Simulation 14 (3), 1153-1173, 2016	1287	2016
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	765	2020
Active learning of linearly parametrized interatomic potentials EV Podryabinkin, AV Shapeev Computational Materials Science 140, 171-180, 2017	596	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	540	2020
Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles B Mortazavi, B Javvaji, F Shojaei, T Rabczuk, AV Shapeev, X Zhuang Nano Energy 82, 105716, 2021	480	2021
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	382	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	329	2019
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	267	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	194	2020
Machine learning of molecular properties: Locality and active learning K Gubaev, EV Podryabinkin, AV Shapeev The Journal of chemical physics 148 (24), 2018	187	2018
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	180	2020
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), 55, 2019	169	2019
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	168	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	159	2020
Machine-learned multi-system surrogate models for materials prediction C Nyshadham, M Rupp, B Bekker, AV Shapeev, T Mueller, ... npj Computational Materials 5 (1), 51, 2019	142	2019
Machine-learned interatomic potentials for alloys and alloy phase diagrams CW Rosenbrock, K Gubaev, AV Shapeev, LB Pártay, N Bernstein, ... npj Computational Materials 7 (1), 24, 2021	122	2021
Accessing thermal conductivity of complex compounds by machine learning interatomic potentials P Korotaev, I Novoselov, A Yanilkin, A Shapeev Physical Review B 100 (14), 144308, 2019	118	2019
Nanoporous C3N4, C3N5 and C3N6 nanosheets; novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties B Mortazavi, F Shojaei, M Shahrokhi, M Azizi, T Rabczuk, AV Shapeev, ... Carbon 167, 40-50, 2020	115	2020
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	110	2019
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	106	2019

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