Search Results - "Shapeev, Alexander"

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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Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles by Mortazavi, Bohayra, Javvaji, Brahmanandam, Shojaei, Fazel, Rabczuk, Timon, Shapeev, Alexander V., Zhuang, Xiaoying

“…Chemical vapor deposition has been most recently employed to fabricate centimeter-scale high-quality single-layer MoSi2N4 (Science; 2020;369; 670). Motivated…”
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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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Performance and Cost Assessment of Machine Learning Interatomic Potentials by Zuo, Yunxing, Chen, Chi, Li, Xiangguo, Deng, Zhi, Chen, Yiming, Behler, Jörg, Csányi, Gábor, Shapeev, Alexander V, Thompson, Aidan P, Wood, Mitchell A, Ong, Shyue Ping

“…Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a…”
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Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials by Mortazavi, Bohayra, Rajabpour, Ali, Zhuang, Xiaoying, Rabczuk, Timon, Shapeev, Alexander V.

“…Examination of thermal expansion of two-dimensional (2D) nanomaterials is a challenging theoretical task with either ab-initio or classical molecular dynamics…”
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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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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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Nanoporous C3N4, C3N5 and C3N6 nanosheets; novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties by Mortazavi, Bohayra, Shojaei, Fazel, Shahrokhi, Masoud, Azizi, Maryam, Rabczuk, Timon, Shapeev, Alexander V., Zhuang, Xiaoying

“…Carbon nitride two-dimensional (2D) materials are among the most attractive class of nanomaterials, with wide range of application prospects. As a continuous…”
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A machine-learning-based investigation on the mechanical/failure response and thermal conductivity of semiconducting BC2N monolayers by Mortazavi, Bohayra, Novikov, Ivan S., Shapeev, Alexander V.

“…Graphene-like lattices consisting of neighboring elements of boron, carbon and nitrogen are currently among the most attractive two-dimensional (2D)…”
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Anisotropic and outstanding mechanical, thermal conduction, optical, and piezoelectric responses in a novel semiconducting BCN monolayer confirmed by first-principles and machine learning by Mortazavi, Bohayra, Shojaei, Fazel, Yagmurcukardes, Mehmet, Shapeev, Alexander V., Zhuang, Xiaoying

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Anisotropic mechanical response, high negative thermal expansion, and outstanding dynamical stability of biphenylene monolayer revealed by machine-learning interatomic potentials by Mortazavi, Bohayra, Shapeev, Alexander V.

“…[Display omitted] •Machine-learning interatomic potentials are developed to study biphenylene monolayer.•MLIP-based models accurately reproduce anisotropic…”
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A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides by Mortazavi, Bohayra, Shojaei, Fazel, Shapeev, Alexander V., Zhuang, Xiaoying

“…Carbon nitride nanoporous lattices are nowadays among the most appealing two-dimensional (2D) nanomaterials for diverse cutting-edge technologies. In one of…”
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Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials by Kostiuchenko, Tatiana, Körmann, Fritz, Neugebauer, Jörg, Shapeev, Alexander

“…Recently, high-entropy alloys (HEAs) have attracted wide attention due to their extraordinary materials properties. A main challenge in identifying new HEAs is…”
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Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys by Jafary-Zadeh, Mehdi, Khoo, Khoong Hong, Laskowski, Robert, Branicio, Paulo S., Shapeev, Alexander V.

“…The concept of local lattice distortion (LLD) is of fundamental importance in the understanding of properties of high-entropy alloys and, more generally, of…”
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Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe by Novikov, Ivan, Grabowski, Blazej, Körmann, Fritz, Shapeev, Alexander

“…We present the magnetic Moment Tensor Potentials (mMTPs), a class of machine-learning interatomic potentials, accurately reproducing both vibrational and…”
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Validation of moment tensor potentials for fcc and bcc metals using EXAFS spectra by Shapeev, Alexander V., Bocharov, Dmitry, Kuzmin, Alexei

“…Machine-learning potentials for materials, namely the moment tensor potentials (MTPs), were validated using experimental EXAFS spectra for the first time. The…”
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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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Constrained Density Functional Theory: A Potential-Based Self-Consistency Approach by Gonze, Xavier, Seddon, Benjamin, Elliott, James A., Tantardini, Christian, Shapeev, Alexander V.

“…Chemical reactions, charge transfer reactions, and magnetic materials are notoriously difficult to describe within Kohn–Sham density functional theory, which…”
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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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