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Nanohardness from First Principles with Active Learning on Atomic Environments.
J Chem Theory Comput. 2022 Feb 8;18(2):1109-1121. doi: 10.1021/acs.jctc.1c00783. Epub 2022 Jan 6.
J Chem Theory Comput. 2022.
PMID: 34990122
Machine learning of molecular properties: Locality and active learning.
Gubaev K, Podryabinkin EV, Shapeev AV.
Gubaev K, et al. Among authors: podryabinkin ev.
J Chem Phys. 2018 Jun 28;148(24):241727. doi: 10.1063/1.5005095.
J Chem Phys. 2018.
PMID: 29960350
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Young's Modulus and Tensile Strength of Ti3C2 MXene Nanosheets As Revealed by In Situ TEM Probing, AFM Nanomechanical Mapping, and Theoretical Calculations.
Firestein KL, von Treifeldt JE, Kvashnin DG, Fernando JFS, Zhang C, Kvashnin AG, Podryabinkin EV, Shapeev AV, Siriwardena DP, Sorokin PB, Golberg D.
Firestein KL, et al. Among authors: podryabinkin ev.
Nano Lett. 2020 Aug 12;20(8):5900-5908. doi: 10.1021/acs.nanolett.0c01861. Epub 2020 Jul 13.
Nano Lett. 2020.
PMID: 32633975
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First-Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine-Learning Interatomic Potentials.
Mortazavi B, Silani M, Podryabinkin EV, Rabczuk T, Zhuang X, Shapeev AV.
Mortazavi B, et al. Among authors: podryabinkin ev.
Adv Mater. 2021 Sep;33(35):e2102807. doi: 10.1002/adma.202102807. Epub 2021 Jul 23.
Adv Mater. 2021.
PMID: 34296779
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