Alexander Shapeev

Citat de

	Toate	Din 2020
Referințe bibliografice	8944	8148
h-index	42	41
i10-index	83	72

2500

1250

625

1875

20112012201320142015201620172018201920202021202220232024202524 37 59 74 38 65 88 134 241 649 1043 1471 2003 2454 525

Acces public

Afișați-le pe toate

59 de articole

2 articole

disponibile

indisponibile

Pe baza cerințelor privind finanțarea

Coautori

Evgeny PodryabinkinSkolkovo Institute of Science and TechnologyAdresă de e-mail confirmată pe skoltech.ru
Bohayra MortazaviLeibniz Universität HannoverAdresă de e-mail confirmată pe iop.uni-hannover.de
Xiaoying ZhuangLeibniz University HanoverAdresă de e-mail confirmată pe iop.uni-hannover.de
Ivan NovikovSenior Research Scientist, Skolkovo Institute of Science and TechnologyAdresă de e-mail confirmată pe skoltech.ru
Timon RabczukBauhaus University WeimarAdresă de e-mail confirmată pe uni-weimar.de
Jörg NeugebauerDirector, Max-Planck-Institut für EisenforschungAdresă de e-mail confirmată pe mpie.de
Fritz KörmannUniversity of Stuttgart, ICAMS Bochum, MPIE DüsseldorfAdresă de e-mail confirmată pe mpie.de
Evgenii TsymbalovApptekAdresă de e-mail confirmată pe apptek.com
Gus L. W. HartBrigham Young University, Physics and AstronomyAdresă de e-mail confirmată pe byu.edu
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeAdresă de e-mail confirmată pe cam.ac.uk
Christoph OrtnerUniversity of British ColumbiaAdresă de e-mail confirmată pe math.ubc.ca
Blazej GrabowskiInstitute for Materials Science, University of StuttgartAdresă de e-mail confirmată pe imw.uni-stuttgart.de
Mitchell LuskinProfessor of Mathematics, University of MinnesotaAdresă de e-mail confirmată pe umn.edu
Ming DaoMassachusetts Institute of TechnologyAdresă de e-mail confirmată pe mit.edu
Conrad RosenbrockBrigham Young UniversityAdresă de e-mail confirmată pe byu.edu
Brahmanandam JavvajiPostdoc Scientific Coworker, Chair of Computational Science and Simulation Technology, Insisute ofAdresă de e-mail confirmată pe iop.uni-hannover.de
Alexander G. KvashninFull Professor, Skolkovo Institute of Science and TechnologyAdresă de e-mail confirmată pe skoltech.ru
Stephan RocheICREA Research Professor, Catalan Institute of Nanoscience and NanotechnologyAdresă de e-mail confirmată pe icn2.cat
Max HodappMaterials Center Leoben Forschung GmbH (MCL)Adresă de e-mail confirmată pe mcl.at
Jörg BehlerRuhr-Universität BochumAdresă de e-mail confirmată pe ruhr-uni-bochum.de

Urmăriți

Alexander Shapeev

Skolkovo Institute of Science and Technology

Adresă de e-mail confirmată pe shapeev.com - Pagina de pornire

Applied Mathematics Numerical Analysis Scientific Computing Materials Science


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Moment tensor potentials: A class of systematically improvable interatomic potentials AV Shapeev Multiscale Modeling & Simulation 14 (3), 1153-1173, 2016	1276	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	762	2020
Active learning of linearly parametrized interatomic potentials EV Podryabinkin, AV Shapeev Computational Materials Science 140, 171-180, 2017	592	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	534	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	476	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	375	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	263	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	193	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
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	167	2021
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	167	2019
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	121	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

Sistemul nu poate realiza operația în acest moment. Încercați din nou mai târziu.

Articole 1–20

Referințe bibliografice pe an

Citate duplicat

Citate fuzionate

Adăugați coautoriCoautori

Urmăriți

Citat de

Coautori