Alexander V. Shapeev

Skolkovo Institute of Science and Technology — Russia

Materials Science · Physical Sciences

41h-index8.5kcitations183works3.62yr avg
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Research Topics

Machine Learning in Materials Science(90), X-ray Diffraction in Crystallography(26), Microstructure and mechanical properties(20), Graphene research and applications(15), Metal and Thin Film Mechanics(14)

Publications183 total

Unified approach to generating a training set for machine learning interatomic potentials: The case of BCC tungsten
Materials Today Communications·2025· 3 cited
Actively-trained magnetic Moment Tensor Potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN
arXiv (Cornell University)·2024Open Access
Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning
arXiv (Cornell University)·2024Open Access
Roadmap for the development of machine learning-based interatomic potentials
Modelling and Simulation in Materials Science and Engineering·2024· 35 citedOpen Access
Exhaustive search for novel multicomponent alloys with brute force and machine learning
npj Computational Materials·2024· 3 citedOpen Access
Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations
npj Computational Materials·2024· 7 citedOpen Access
Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential
arXiv (Cornell University)·2024Open Access
Towards reliable calculations of thermal rate constants: Ring polymer molecular dynamics for the OH + HBr <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg" display="inline" id="d1e680"><mml:mo>→</mml:mo></mml:math> Br + H<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg" display="inline" id="d1e685"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>O reaction
Chemical Physics Letters·2024· 5 cited
Equivariant tensor network potentials
Machine Learning Science and Technology·2024· 5 citedOpen Access
Construction of Machine Learning Interatomic Potentials for Metals
Russian Physics Journal·2024· 6 cited
Fitting to magnetic forces improves the reliability of magnetic Moment Tensor Potentials
Computational Materials Science·2024· 7 citedOpen Access
Thermophysical properties of Molten FLiNaK: A moment tensor potential approach
Journal of Molecular Liquids·2024· 15 cited
A moment tensor potential for lattice thermal conductivity calculations of <i>α</i> and <i>β</i> phases of Ga2O3
Journal of Applied Physics·2024· 11 citedOpen Access
Interatomic Interaction Models for Magnetic Materials: Recent Advances
arXiv (Cornell University)·2024· 1 citedOpen Access
Mechanical Properties of Single and Polycrystalline Solids from Machine Learning
Advanced Theory and Simulations·2024· 11 cited
A moment tensor potential for lattice thermal conductivity calculations of alpha and beta phases of Ga2O3
arXiv (Cornell University)·2024Open Access
Thermophysical Properties of Molten FLiNaK: a Moment Tensor Potential Approach
arXiv (Cornell University)·2024Open Access
Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom
npj Computational Materials·2024· 8 citedOpen Access
Bayesian inference of composition-dependent phase diagrams
Physical review. B./Physical review. B·2023· 6 cited
Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al
Scientific Reports·2023· 23 citedOpen Access
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Frequent Co-authors

Bohayra Mortazavi(15), Xiaoying Zhuang(13), Evgeny V. Podryabinkin(12), Timon Rabczuk(9), Fazel Shojaei(6), Ivan S. Novikov(6), Fritz Körmann(6), Jörg Neugebauer(5), Evgenii Tsymbalov(4), Ju Li(4), Tatiana Kostiuchenko(3), Masoud Shahrokhi(3), I.I. Novoselov(3), Zhe Shi(3), Ming Dao(3), Subra Suresh(3), A. V. Yanilkin(3), Christoph Ortner(3), Jörg Behler(2), Gábor Cśanyi(2)