Search Results - "Podryabinkin, Evgeny V"

Active learning of linearly parametrized interatomic potentials by Podryabinkin, Evgeny V., Shapeev, Alexander V.

“…[Display omitted] This paper introduces an active learning approach to the fitting of machine learning interatomic potentials. Our approach is based on the…”
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Elinvar effect in β-Ti simulated by on-the-fly trained moment tensor potential by Shapeev, Alexander V, Podryabinkin, Evgeny V, Gubaev, Konstantin, Tasnádi, Ferenc, Abrikosov, Igor A

“…A combination of quantum mechanics calculations with machine learning techniques can lead to a paradigm shift in our ability to predict materials properties…”
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First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials by Mortazavi, Bohayra, Silani, Mohammad, Podryabinkin, Evgeny V., Rabczuk, Timon, Zhuang, Xiaoying, Shapeev, Alexander V.

“…Density functional theory calculations are robust tools to explore the mechanical properties of pristine structures at their ground state but become…”
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The MLIP package: moment tensor potentials with MPI and active learning by Novikov, Ivan S, Gubaev, Konstantin, Podryabinkin, Evgeny V, Shapeev, Alexander V

“…The subject of this paper is the technology (the 'how') of constructing machine-learning interatomic potentials, rather than science (the 'what' and 'why') of…”
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High thermal conductivity in semiconducting Janus and non-Janus diamanes by Raeisi, Mostafa, Mortazavi, Bohayra, Podryabinkin, Evgeny V., Shojaei, Fazel, Zhuang, Xiaoying, Shapeev, Alexander V.

“…Most recently, F-diamane monolayer was experimentally realized by the fluorination of bilayer graphene. In this work we elaborately explore the electronic and…”
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A machine-learning potential-based generative algorithm for on-lattice crystal structure prediction by Sotskov, Vadim, Podryabinkin, Evgeny V., Shapeev, Alexander V.

“…We propose a crystal structure prediction method based on a novel structure generation algorithm and on-lattice machine-learning interatomic potentials. Our…”
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Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials by Mortazavi, Bohayra, Novikov, Ivan S., Podryabinkin, Evgeny V., Roche, Stephan, Rabczuk, Timon, Shapeev, Alexander V., Zhuang, Xiaoying

“…•Machine-learning interatomic potentials (MLIPs) could accurately examine the phononic properties.•MLIPs can substitute the standard DFT-based methods for the…”
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Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials by Mortazavi, Bohayra, Podryabinkin, Evgeny V, Novikov, Ivan S, Roche, Stephan, Rabczuk, Timon, Zhuang, Xiaoying, Shapeev, Alexander V

“…It is well-known that the calculation of thermal conductivity using classical molecular dynamics (MD) simulations strongly depends on the choice of the…”
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Nanohardness from First Principles with Active Learning on Atomic Environments by Podryabinkin, Evgeny V, Kvashnin, Alexander G, Asgarpour, Milad, Maslenikov, Igor I, Ovsyannikov, Danila A, Sorokin, Pavel B, Popov, Mikhail Yu, Shapeev, Alexander V

“…We propose a methodology for the calculation of nanohardness by atomistic simulations of nanoindentation. The methodology is enabled by machine-learning…”
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Accelerating high-throughput searches for new alloys with active learning of interatomic potentials by Gubaev, Konstantin, Podryabinkin, Evgeny V., Hart, Gus L.W., Shapeev, Alexander V.

“…[Display omitted] •Alloy ground state search accelerated by orders of magnitude via machine learning.•The acceleration is achieved by screening with an…”
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Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution by Mortazavi, Bohayra, Podryabinkin, Evgeny V., Novikov, Ivan S., Rabczuk, Timon, Zhuang, Xiaoying, Shapeev, Alexander V.

“…Accurate evaluation of the thermal conductivity of a material can be a challenging task from both experimental and theoretical points of view. In particular…”
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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 by Firestein, Konstantin L, von Treifeldt, Joel E, Kvashnin, Dmitry G, Fernando, Joseph F. S, Zhang, Chao, Kvashnin, Alexander G, Podryabinkin, Evgeny V, Shapeev, Alexander V, Siriwardena, Dumindu P, Sorokin, Pavel B, Golberg, Dmitri

“…Two-dimensional transition metal carbides, that is, MXenes and especially Ti3C2, attract attention due to their excellent combination of properties. Ti3C2…”
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Mechanical Properties of Single and Polycrystalline Solids from Machine Learning by Jalolov, Faridun N., Podryabinkin, Evgeny V., Oganov, Artem R., Shapeev, Alexander V., Kvashnin, Alexander G.

“…Calculating the elastic and mechanical characteristics of non‐crystalline solids can be challenging due to the high computational cost of ab initio methods and…”
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Young’s Modulus and Tensile Strength of Ti 3 C 2 MXene Nanosheets As Revealed by In Situ TEM Probing, AFM Nanomechanical Mapping, and Theoretical Calculations by Firestein, Konstantin L., von Treifeldt, Joel E., Kvashnin, Dmitry G., Fernando, Joseph F. S., Zhang, Chao, Kvashnin, Alexander G., Podryabinkin, Evgeny V., Shapeev, Alexander V., Siriwardena, Dumindu P., Sorokin, Pavel B., Golberg, Dmitri

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A machine learning potential-based generative algorithm for on-lattice crystal structure prediction by Sotskov, Vadim, Shapeev, Alexander V, Podryabinkin, Evgeny V

“…We propose a method for crystal structure prediction based on a new structure generation algorithm and on-lattice machine learning interatomic potentials. Our…”
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Mechanical properties of single and polycrystalline solids from machine learning by Jalolov, Faridun N, Podryabinkin, Evgeny V, Oganov, Artem R, Shapeev, Alexander V, Kvashnin, Alexander G

“…Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of $ab$ $initio$ methods and low…”
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Active learning of linearly parametrized interatomic potentials by Podryabinkin, Evgeny V, Shapeev, Alexander V

“…Computational Materials Science, volume 140, pages 171-180, 2017 This paper introduces an active learning approach to the fitting of machine learning…”
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The MLIP package: Moment Tensor Potentials with MPI and Active Learning by Novikov, Ivan S, Gubaev, Konstantin, Podryabinkin, Evgeny V, Shapeev, Alexander V

“…The subject of this paper is the technology (the "how") of constructing machine-learning interatomic potentials, rather than science (the "what" and "why") of…”
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Machine-Learning Interatomic Potentials Enable First-Principles Multiscale Modeling of Lattice Thermal Conductivity in Graphene/Borophene Heterostructures by Mortazavi, Bohayra, Podryabinkin, Evgeny V, Roche, Stephan, Rabczuk, Timon, Zhuang, Xiaoying, Shapeev, Alexander V

“…Materials Horizons 2020 One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with…”
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High thermal conductivity in semiconducting Janus and non-Janus diamanes by Raeisi, Mostafa, Mortazavi, Bohayra, Podryabinkin, Evgeny V, Shojaei, Fazel, Zhuang, Xiaoying, Shapeev, Alexander V

“…Carbon 2020 Most recently, F-diamane monolayer was experimentally realized by the fluorination of bilayer graphene. In this work we elaborately explore the…”
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