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Publications of A. V. Shapeev
Book Chapters
[1]Applications of Machine Learning for Representing Interatomic Interactions (Alexander V. Shapeev), Chapter in Computational Materials Discovery (Artem R Oganov, Gabriele Saleh, Alexander G Kvashnin, eds.), The Royal Society of Chemistry, 2019. [bibtex]
Refereed Articles
[42]Performance and Cost Assessment of Machine Learning Interatomic Potentials (Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V Shapeev, Aidan P Thompson, Mitchell A Wood, others), The Journal of Physical Chemistry A, ACS Publications, volume 124, pages 731--745, 2020. [bibtex] [arXiv]
[41]Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials (Bohayra Mortazavi, Ivan S Novikov, Evgeny V Podryabinkin, Stephan Roche, Timon Rabczuk, Alexander V Shapeev, Xiaoying Zhuang), Applied Materials Today, Elsevier, volume 20, pages 100685, 2020. [bibtex] [pdf] [doi]
[40]Efficient machine-learning based interatomic potentials for exploring thermal conductivity in two-dimensional materials (Bohayra Mortazavi, Evgeny Podryabinkin, Ivan S Novikov, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander Shapeev), Journal of Physics: Materials, IOP Publishing, 2020. [bibtex] [doi]
[39]Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution (Bohayra Mortazavi, Evgeny V Podryabinkin, Ivan S Novikov, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev), Computer Physics Communications, Elsevier, pages 107583, 2020. [bibtex] [pdf] [doi]
[38]Lattice dynamics simulation using machine learning interatomic potentials (VV Ladygin, P Yu Korotaev, AV Yanilkin, AV Shapeev), Computational Materials Science, Elsevier, volume 172, pages 109333, 2020. [bibtex] [pdf] [doi]
[37]Deep elastic strain engineering of bandgap through machine learning (Zhe Shi, Evgenii Tsymbalov, Ming Dao, Subra Suresh, Alexander Shapeev, Ju Li), Proceedings of the National Academy of Sciences, National Acad Sciences, volume 116, pages 4117--4122, 2019. [bibtex] [pdf] [doi]
[36]Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning (Evgeny V Podryabinkin, Evgeny V Tikhonov, Alexander V Shapeev, Artem R Oganov), Physical Review B, APS, volume 99, pages 064114, 2019. [bibtex] [pdf] [doi] [arXiv]
[35]Machine-learned multi-system surrogate models for materials prediction (Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker, Alexander V Shapeev, Tim Mueller, Conrad W Rosenbrock, Gábor Csányi, David W Wingate, Gus LW Hart), npj Computational Materials, Nature Publishing Group, volume 5, pages 51, 2019. [bibtex] [pdf] [doi] [arXiv]
[34]Improving accuracy of interatomic potentials: more physics or more data? A case study of silica (Ivan S Novikov, Alexander V Shapeev), Materials Today Communications, Elsevier, volume 18, pages 74--80, 2019. [bibtex] [doi] [arXiv]
[33]Ring polymer molecular dynamics and active learning of moment tensor potential for gas-phase barrierless reactions: Application to S + H2 (Ivan S Novikov, Alexander V Shapeev, Yury V Suleimanov), The Journal of Chemical Physics, AIP Publishing, volume 151, pages 224105, 2019. [bibtex] [pdf] [doi]
[32]Prediction of C7N6 and C9N4: stable and strong porous carbon-nitride nanosheets with attractive electronic and optical properties (Bohayra Mortazavi, Masoud Shahrokhi, Alexander V Shapeev, Timon Rabczuk, Xiaoying Zhuang), Journal of Materials Chemistry C, Royal Society of Chemistry, 2019. [bibtex] [doi]
[31]Sublattice formation in CoCrFeNi high-entropy alloy (E.A. Meshkov, I.I. Novoselov, A.V. Shapeev, A.V. Yanilkin), Intermetallics, Elsevier, volume 112, pages 106542, 2019. [bibtex] [pdf] [doi]
[30]Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials (Tatiana Kostiuchenko, Fritz Koermann, Joerg Neugebauer, Alexander Shapeev), npj Computational Materials, Nature Publishing Group, volume 5, pages 55, 2019. [bibtex] [pdf] [doi] [arXiv]
[29]Accessing thermal conductivity of complex compounds by machine learning interatomic potentials (Pavel Korotaev, Ivan Novoselov, Aleksey Yanilkin, Alexander Shapeev), Physical Review B, APS, volume 100, pages 144308, 2019. [bibtex] [pdf] [doi]
[28]Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys (M. Jafary-Zadeh, K.H. Khoo, R. Laskowski, P.S. Branicio, A. Shapeev), Journal of Alloys and Compounds, Elsevier, 2019. [bibtex] [pdf] [doi]
[27]Ab initio vibrational free energies including anharmonicity for multicomponent alloys (Blazej Grabowski, Yuji Ikeda, Prashanth Srinivasan, Fritz Körmann, Christoph Freysoldt, Andrew Ian Duff, Alexander Shapeev, Jörg Neugebauer), npj Computational Materials, Nature Publishing Groupcat=IP, volume 5, pages 1--6, 2019. [bibtex] [pdf] [doi]
[26]Moment tensor potentials as a promising tool to study diffusion processes (I.I. Novoselov, A.V. Yanilkin, A.V. Shapeev, E.V. Podryabinkin), Computational Materials Science, volume 165, pages 46--56, 2019. [bibtex] [pdf] [doi] [arXiv]
[25]Accelerating high-throughput searches for new alloys with active learning of interatomic potentials (Konstantin Gubaev, Evgeny V. Podryabinkin, Gus L. W. Hart, Alexander V. Shapeev), Computational Materials Science, volume 156, pages 148--156, 2019. [bibtex] [pdf] [doi] [arXiv]
[24]Machine learning of molecular properties: Locality and active learning (Konstantin Gubaev, Evgeny V. Podryabinkin, Alexander V. Shapeev), The Journal of Chemical Physics, AIP Publishing, volume 148, pages 241727, 2018. [bibtex] [pdf] [doi] [arXiv]
[23]Automated calculation of thermal rate coefficients using ring polymer molecular dynamics and machine-learning interatomic potentials with active learning (Ivan S Novikov, Yury V Suleimanov, Alexander V Shapeev), Physical Chemistry Chemical Physics, Royal Society of Chemistry, volume 20, pages 29503--29512, 2018. [bibtex] [doi] [arXiv]
[22]Dropout-Based Active Learning for Regression (Evgenii Tsymbalov, Maxim Panov, Alexander Shapeev), in International Conference on Analysis of Images, Social Networks and Texts, pages 247--258, 2018. [bibtex] [doi] [arXiv]
[21]Approximation of Crystalline Defects at Finite Temperature (Alexander V. Shapeev, Mitchell Luskin), Multiscale Modeling & Simulation, Society for Industrial & Applied Mathematics (SIAM), volume 15, pages 1830--1864, 2017. [bibtex] [pdf] [doi] [arXiv]
[20]Active Learning of Linearly Parametrized Interatomic Potentials (E. V. Podryabinkin, A. V. Shapeev), Computational Materials Science, volume 140, pages 171-180, 2017. [bibtex] [doi] [arXiv]
[19]Accurate representation of formation energies of crystalline alloys with many components (A. Shapeev), Computational Materials Science, volume 139, pages 26-30, 2017. [bibtex] [doi] [arXiv]
[18]Moment Tensor Potentials: a class of systematically improvable interatomic potentials (A.V. Shapeev), Multiscale Model. Simul., volume 14, pages 1153-1173, 2016. [bibtex] [pdf] [doi] [arXiv]
[17]Analysis of an optimization-based atomistic-to-continuum coupling method for point defects (D. Olson, A. V. Shapeev, P. Bochev, M. Luskin), ESAIM: Mathematical Modelling and Numerical Analysis, volume 50, pages 1--41, 2016. [bibtex] [doi] [arXiv]
[16]Analysis of blended atomistic/continuum hybrid methods (X. H. Li, C. Ortner, A. V. Shapeev, B. Van Koten), Numerische Mathematik, Springer Berlin Heidelberg, volume 134, pages 275-326, 2016. [bibtex] [pdf] [doi] [arXiv]
[15]Analysis of Boundary Conditions for Crystal Defect Atomistic Simulations (V. Ehrlacher, C. Ortner, A.V. Shapeev), volume 222, pages 1217-1268, 2016. [bibtex] [doi] [arXiv]
[14]An optimization based coupling method for multiscale problems (A. Abdulle, A. Shapeev O. Jecker), Multiscale Model. Simul., Society for Industrial and Applied Mathematics, volume 14, pages 1377-1416, 2016. [bibtex] [pdf] [doi]
[13]Theory-based Benchmarking of the Blended Force-Based Quasicontinuum Method (Xingjie Li, Mitchell Luskin, Christoph Ortner, Alexander V. Shapeev), Computer Methods in Applied Mechanics and Engineering, volume 268, pages 763--781, 2014. [bibtex] [pdf] [doi] [arXiv]
[12](In-)Stability and Stabilisation of QNL-Type Atomistic-to-Continuum Coupling Methods (C. Ortner, A.V. Shapeev, L. Zhang), Multiscale Model. Simul., volume 12, pages 1258-1293, 2014. [bibtex] [pdf] [doi] [arXiv]
[11]An optimization-based atomistic-to-continuum coupling method (D. Olson, P. Bochev, M. Luskin, A. V. Shapeev), SIAM Journal on Numerical Analysis, Society for Industrial and Applied Mathematics, volume 52, pages 2183--2204, 2014. [bibtex] [arXiv]
[10]Analysis of an energy-based atomistic/continuum approximation of a vacancy in the 2D triangular lattice (C. Ortner, A. V. Shapeev), Math. Comp., volume 82, pages 2191--2236, 2013. [bibtex] [doi] [arXiv]
[9]A Priori and A Posteriori $W^1,infty$ Error Analysis of a QC Method for Complex Lattices (A. Abdulle, P. Lin, A. Shapeev), SIAM J. Numer. Anal., volume 51, pages 2357--2379, 2013. [bibtex] [doi] [arXiv]
[8]Consistent Energy-based Atomistic/Continuum Coupling for Two-body Potentials in Three Dimensions (A. V. Shapeev), SIAM J. Sci. Comput., volume 34, pages B335--B360, 2012. [bibtex] [doi] [arXiv]
[7]The Spectrum of the Force-Based Quasicontinuum Operator for a Homogeneous Periodic Chain (M. Dobson, C. Ortner, A. V. Shapeev), Multiscale Model. Simul., volume 10, pages 744--765, 2012. [bibtex] [doi] [arXiv]
[6]Numerical Methods for Multilattices (A. Abdulle, P. Lin, A. V. Shapeev), Multiscale Model. Simul., volume 10, pages 696--726, 2012. [bibtex] [doi] [arXiv]
[5]Consistent Energy-Based Atomistic/Continuum Coupling for Two-Body Potentials in One and Two Dimensions (A. V. Shapeev), Multiscale Model. Simul., volume 9, pages 905--932, 2011. [bibtex] [doi] [arXiv]
(Winner of 2013 SIAM Outstanding Paper Prize)
[4]An asymptotic fitting finite element method with exponential mesh refinement for accurate computation of corner eddies in viscous flows (A. V. Shapeev, Ping Lin), SIAM J. Sci. Comput., volume 31, pages 1874--1900, 2009. [bibtex] [pdf] [doi]
[3]Investigation of mixed spectral and finite difference approximation on the basis of problem of viscous flow in a diffuser (A. V. Shapeev), Siberian Journal of Numerical Mathematics, volume 8, pages 149--162, 2005. (in Russian) [bibtex]
[2]Unsteady self-similar flow of a viscous incompressible fluid in a plane divergent channel (A. V. Shapeev), Fluid Dynamics, volume 39, pages 36--41, 2004. [bibtex]
[1]High-order accurate difference schemes for elliptic equations in a domain with a curvilinear boundary (A. V. Shapeev, V. P. Shapeev), Computational Mathematics and Mathematical Physics, volume 40, pages 213--221, 2000. [bibtex]
Conference Proceedings
[7]Polynomial Least-Squares Reconstruction for Semi-Lagrangian Cell-Centered Hydrodynamic Schemes (Gilles Carre, Stephane Del Pino, Kirill Pichon Gostaf, Emmanuel Labourasse, Alexander Shapeev), In ESAIM Proceedings, CEMRACS 2008 - Modelling and Numerical Simulation of Complex Fluids, volume 28, pages 100--116, 2009. [bibtex] [pdf]
[6]A numerical-asymptotic method for computation of infinite number of eddies of viscous flows in domains with corners (A. V. Shapeev), In Computational Fluid Dynamics, 2008. [bibtex]
[5]Numerical Simulation of 3D Motions of Locally Heated Liquid Films (A. V. Shapeev), In Proceedings of the Third Russian National Conference on Heat Transfer, pages 103--105, 2002. (in Russian) [bibtex]
[4]Proof of Convergence of Numerical Methods of Solving Weakly Nonlinear Problems (A. V. Shapeev), In Problems of Continuous Media Mechanics: Proceedings of the 33rd Regional Youth Conference, pages 189--193, 2002. (in Russian) [bibtex]
[3]Unconditionally Stable Explicit High-Order Scheme for the Nonlinear Schrodinger Equation (A. V. Shapeev), In Proceedings of the Youth Scientific Conference dedicated to the 10th anniversary of Institute of Computational Technologies SB RAS, volume 2, pages 175--179, 2001. (in Russian) [bibtex]
[2]Solution of Elliptical Problems with Singularities using High-Order Schemes (A. V. Shapeev, V. P. Shapeev), In Problems of Continuous Media Mechanics: Proceedings of the 32nd Regional Youth Conference, pages 62--66, 2001. (in Russian) [bibtex]
[1]High order approximation schemes (A. V. Shapeev, V. P. Shapeev), In Proceedings of the 3rd European Conference on Numerical Mathematics and Advanced Applications, World Scientific, pages 715--724, 2000. [bibtex]
Other Publications
[6]Development of an Optimization-Based Atomistic-to-Continuum Coupling Method (Derek Olson, Pavel Bochev, Mitchell Luskin, Alexander V. Shapeev), 2013. [bibtex] [arXiv]
[5]Interpolants of Lattice Functions for the Analysis of Atomistic/Continuum Multiscale Methods (C. Ortner, A. V. Shapeev), 2012. (arXiv:1204.3705) [bibtex] [pdf]
[4]Homogenization-based Analysis of Quasicontinuum Method for Complex Crystals (A. Abdulle, P. Lin, A. V. Shapeev), 2010. (arXiv:1006.0378) [bibtex] [pdf]
[3]Viscous incompressible axisymmetric flows in cones (A. V. Shapeev), Technical report, Lavrentyev Institute of Hydrodynamics SB RAS, 2009. (In Russian) [bibtex]
[2]Energy-based ghost force removing techniques for the quasicontinuum method (P. Lin, A. V. Shapeev), 2009. (arXiv:0909.5437) [bibtex] [pdf]
[1]Pattern Formation -- Group Theoretical and Numerical Points of View (G. Czichowski, A. V. Shapeev, V. P Shapeev), Technical report, Ernst-Moritz-Arndt-Universitat Greifswald, Institut fur Mathematik und Informatik, 2002. [bibtex]