| Moment tensor potentials: A class of systematically improvable interatomic potentials AV Shapeev Multiscale Modeling & Simulation 14 (3), 1153-1173, 2016 | 1206 | 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 | 710 | 2020 |
| Active learning of linearly parametrized interatomic potentials EV Podryabinkin, AV Shapeev Computational Materials Science 140, 171-180, 2017 | 566 | 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 | 491 | 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 | 450 | 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 | 359 | 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 | 318 | 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 | 240 | 2021 |
| Machine learning of molecular properties: Locality and active learning K Gubaev, EV Podryabinkin, AV Shapeev The Journal of chemical physics 148 (24), 2018 | 184 | 2018 |
| 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 | 182 | 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 | 171 | 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 | 160 | 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 | 155 | 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 | 142 | 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 | 136 | 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 | 117 | 2021 |
| 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), 80, 2019 | 110 | 2019 |
| 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 | 108 | 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 | 103 | 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 | 102 | 2019 |
Evgeny PodryabinkinSkolkovo Institute of Science and Technology在 skoltech.ru 的电子邮件经过验证
