| Active learning of linearly parametrized interatomic potentials EV Podryabinkin, AV Shapeev Computational Materials Science 140, 171-180, 2017 | 598 | 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 | 542 | 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 | 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 |
| 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 |
| 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 |
| 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 | 85 | 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 | 59 | 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 | 44 | 2020 |
| 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 | 35 | 2020 |
| MLIP-3: Active learning on atomic environments with moment tensor potentials E Podryabinkin, K Garifullin, A Shapeev, I Novikov The Journal of Chemical Physics 159 (8), 2023 | 33 | 2023 |
| 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 | 32 | 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 | 31 | 2017 |
| 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 | 24 | 2013 |
| Active learning and uncertainty estimation A Shapeev, K Gubaev, E Tsymbalov, E Podryabinkin Machine Learning Meets Quantum Physics, 309-329, 2020 | 23 | 2020 |
