Zobrazeno 1 - 10 of 22 pro vyhledávání: '"Evgeny V Podryabinkin"'
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Akademický článek
Elinvar effect in β-Ti simulated by on-the-fly trained moment tensor potential
Autor: Alexander V Shapeev, Evgeny V Podryabinkin, Konstantin Gubaev, Ferenc Tasnádi, Igor A Abrikosov
Publikováno v: New Journal of Physics, Vol 22, Iss 11, p 113005 (2020)
A combination of quantum mechanics calculations with machine learning techniques can lead to a paradigm shift in our ability to predict materials properties from first principles. Here we show that on-the-fly training of an interatomic potential desc
Externí odkaz: https://doaj.org/article/dad481a1d3cb40bf971b5690f4e0a277
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Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning
Autor: Alexander V. Shapeev, Evgeny V. Podryabinkin, Jun Ding, Evan Ma, Qi Wang, Longfei Zhang
Publikováno v: npj Computational Materials, Vol 6, Iss 1, Pp 1-12 (2020)
The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated proces
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49edb37218c1e637f9a1ae26223e2bb5
https://doaj.org/article/607c7977330949b6958952bb11789513
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3
Young’s Modulus and Tensile Strength of Ti3C2 MXene Nanosheets As Revealed by In Situ TEM Probing, AFM Nanomechanical Mapping, and Theoretical Calculations
Autor: Evgeny V. Podryabinkin, Dmitri Golberg, Konstantin L. Firestein, Alexander V. Shapeev, Chao Zhang, Pavel B. Sorokin, Joel E. von Treifeldt, Alexander G. Kvashnin, Joseph F. S. Fernando, Dmitry G. Kvashnin, Dumindu P. Siriwardena
Publikováno v: Nano Letters. 20:5900-5908
Two-dimensional transition metal carbides, that is, MXenes and especially Ti3C2, attract attention due to their excellent combination of properties. Ti3C2 nanosheets could be the material of choice for future flexible electronics, energy storage, and
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::debb86393c89a16a72898638e8d23555
https://doi.org/10.1021/acs.nanolett.0c01861
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4
Nanohardness from First Principles with Active Learning on Atomic Environments
Autor: Evgeny V. Podryabinkin, Alexander G. Kvashnin, Milad Asgarpour, Igor I. Maslenikov, Danila A. Ovsyannikov, Pavel B. Sorokin, Mikhail Yu Popov, Alexander V. Shapeev
Publikováno v: Journal of chemical theory and computation. 18(2)
We propose a methodology for the calculation of nanohardness by atomistic simulations of nanoindentation. The methodology is enabled by machine-learning interatomic potentials fitted on the fly to quantum-mechanical calculations of local fragments of
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::480695284a1fc98cf15b572573d47461
https://pubmed.ncbi.nlm.nih.gov/34990122
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5
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials
Autor: Evgeny V. Podryabinkin, Gus L. W. Hart, Konstantin Gubaev, Alexander V. Shapeev
Publikováno v: Computational Materials Science. 156:148-156
We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our approach sig
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74a60aa0bf7c4c5312567f4abfc8c0d8
https://doi.org/10.1016/j.commatsci.2018.09.031
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6
Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
Autor: Bohayra Mortazavi, Alexander V. Shapeev, Timon Rabczuk, Evgeny V. Podryabinkin, Stephan Roche, Xiaoying Zhuang
Publikováno v: Recercat: Dipósit de la Recerca de Catalunya
Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Recercat. Dipósit de la Recerca de Catalunya
instname
Dipòsit Digital de Documents de la UAB
Universitat Autònoma de Barcelona
Digital.CSIC. Repositorio Institucional del CSIC
One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as density functional theory (DFT) provide the best
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c5dc3496e9f881ab69b38d95bc0632c
http://hdl.handle.net/2072/444844
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8
High thermal conductivity in semiconducting Janus and non-Janus diamanes
Autor: Xiaoying Zhuang, Fazel Shojaei, Mostafa Raeisi, Alexander V. Shapeev, Bohayra Mortazavi, Evgeny V. Podryabinkin
Most recently, F-diamane monolayer was experimentally realized by the fluorination of bilayer graphene. In this work we elaborately explore the electronic and thermal conductivity responses of diamane lattices with homo or hetero functional groups, i
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::820bd05013eab7bf057619a6fa8a9f18
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9
Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials
Autor: Alexander V. Shapeev, Evgeny V. Podryabinkin, Ivan S. Novikov, Bohayra Mortazavi, Timon Rabczuk, Stephan Roche, Xiaoying Zhuang
Publikováno v: Dipòsit Digital de Documents de la UAB
Universitat Autònoma de Barcelona
It is well-known that the calculation of thermal conductivity using classical molecular dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic potentials. As proven for the case of graphene, while most of the availabl
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47549041e45fdc6bdaf71cf4a3666612
https://ddd.uab.cat/record/250141
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10
Active Learning and Uncertainty Estimation
Autor: Evgeny V. Podryabinkin, Konstantin Gubaev, Alexander V. Shapeev, Evgenii Tsymbalov
Publikováno v: Machine Learning Meets Quantum Physics ISBN: 9783030402440
Active learning refers to collections of algorithms of systematically constructing the training dataset. It is closely related to uncertainty estimation—we, generally, do not need to train our model on samples on which our prediction already has lo
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::009332b8f038cf8380e5a5e96bdc6d75
https://doi.org/10.1007/978-3-030-40245-7_15
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