List of Publications

Peer-Reviewed Publications:

2024

Construction of Machine Learning Interatomic Potentials for Metals (SV Dmitriev, AA Kistanov, IV Kosarev, SA Scherbinin, AV Shapeev), Russian Physics Journal, Springer International Publishing, volume 67, pages 1408–1413, 2024. [doi]
Equivariant tensor network potentials (Max Hodapp, A Shapeev), Machine Learning: Science and Technology, IOP Publishing, volume 5, pages 035075, 2024. [pdf][doi]
Towards reliable calculations of thermal rate constants: Ring polymer molecular dynamics for the OH+ HBr→ Br+ H2O reaction (Ivan S Novikov, Edgar M Makarov, Yury V Suleimanov, Alexander V Shapeev), Chemical Physics Letters, North-Holland, volume 856, pages 141620, 2024. [arxiv][doi]
Thermophysical properties of Molten FLiNaK: A moment tensor potential approach (Nikita Rybin, Dmitrii Maksimov, Yuriy Zaikov, Alexander Shapeev), Journal of Molecular Liquids, Elsevier, volume 410, pages 125402, 2024. [arxiv][doi]
A moment tensor potential for lattice thermal conductivity calculations of α and β phases of Ga2O3 (Nikita Rybin, Alexander Shapeev), Journal of Applied Physics, AIP Publishing, volume 135, pages 205108, 2024. [arxiv][doi]
Interatomic Interaction Models for Magnetic Materials: Recent Advances (Tatiana S Kostiuchenko, Alexander V Shapeev, Ivan S Novikov), Chinese Physics Letters, Chinese Physical Society and IOP Publishing, volume 41, pages 066101, 2024. [arxiv][doi]
Mechanical properties of single and polycrystalline solids from machine learning (Faridun N Jalolov, Evgeny V Podryabinkin, Artem R Oganov, Alexander V Shapeev, Alexander G Kvashnin), Advanced Theory and Simulations, volume 7, pages 2301171, 2024. [arxiv][doi]
Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom (Prashanth Srinivasan, David Demuriya, Blazej Grabowski, Alexander Shapeev), npj Computational Materials, Nature Publishing Group UK, volume 10, pages 41, 2024. [doi]

2023

A machine-learning potential-based generative algorithm for on-lattice crystal structure prediction (Vadim Sotskov, Evgeny V Podryabinkin, Alexander V Shapeev), Journal of Materials Research, Springer International Publishing, volume 38, pages 5161-5170, 2023. [arxiv][doi]
Constrained DFT-based magnetic machine-learning potentials for magnetic alloys:ca case study of Fe–Al (Alexey S Kotykhov, Konstantin Gubaev, Max Hodapp, Christian Tantardini, Alexander V Shapeev, Ivan S Novikov), Scientific Reports, Nature Publishing Group UK, volume 13, pages 19728, 2023. [pdf][doi]
Accurate melting point prediction through autonomous physics-informed learning (Olga Klimanova, Timofei Miryashkin, Alexander Shapeev), Physical Review B, American Physical Society, volume 108, pages 184103, 2023. [arxiv][doi]
Bayesian inference of composition-dependent phase diagrams (Timofei Miryashkin, Olga Klimanova, Vladimir Ladygin, Alexander Shapeev), Physical Review B, American Physical Society, volume 108, pages 174103, 2023. [arxiv][doi]
MLIP-3: Active learning on atomic environments with moment tensor potentials (Evgeny Podryabinkin, Kamil Garifullin, Alexander Shapeev, Ivan Novikov), The Journal of Chemical Physics, AIP Publishing, volume 159, pages 084112, 2023. [arxiv][doi]
Machine learning-driven synthesis of TiZrNbHfTaC5 high-entropy carbide (Alexander Ya Pak, Vadim Sotskov, Arina A Gumovskaya, Yuliya Z Vassilyeva, Zhanar S Bolatova, Yulia A Kvashnina, Gennady Ya Mamontov, Alexander V Shapeev, Alexander G Kvashnin), npj Computational Materials, Nature Publishing Group UK, volume 9, pages 7, 2023. [arxiv][doi]
Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials (Bohayra Mortazavi,Xiaoying Zhuang, Timon Rabczukd and Alexander V. Shapeev), Materials Horizons, Royal Society of Chemistry, volume 10, pages 1956-1968, 2023. [pdf][doi]
Anharmonicity in bcc refractory elements: A detailed ab initio analysis (Prashanth Srinivasan, Alexander Shapeev, Jörg Neugebauer, Fritz Körmann, and Blazej Grabowski ), Physical Review B, American Physical Society, volume 107, pages 014301, 2023. [pdf][doi]

2022

Validation of moment tensor potentials for fcc and bcc metals using EXAFS spectra (Alexander V Shapeev, Dmitry Bocharov, Alexei Kuzmin), Computational Materials Science, Elsevier, volume 210, pages 111028, 2022. [bibtex]
Nanohardness from First Principles with Active Learning on Atomic Environments (Evgeny V Podryabinkin, Alexander G Kvashnin, Milad Asgarpour, Igor I Maslenikov, Danila A Ovsyannikov, Pavel B Sorokin, Mikhail Yu Popov, Alexander V Shapeev), Journal of Chemical Theory and Computation, ACS Publications, volume 18, pages 1109–1121, 2022. [bibtex]
Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe (Ivan Novikov, Blazej Grabowski, Fritz Körmann, Alexander Shapeev), npj Computational Materials, Nature Publishing Group UK London, volume 8, pages 13, 2022. [bibtex]
AI-accelerated materials informatics method for the discovery of ductile alloys (Ivan Novikov, Olga Kovalyova, Alexander Shapeev, Max Hodapp), Journal of Materials Research, Springer, volume 37, pages 3491–3504, 2022. [bibtex]
Outstanding thermal conductivity and mechanical properties in the direct gap semiconducting penta-NiN2 monolayer confirmed by first-principles (Bohayra Mortazavi, Xiaoying Zhuang, Timon Rabczuk, Alexander V Shapeev), Physica E: Low-dimensional Systems and Nanostructures, Elsevier, volume 140, pages 115221, 2022. [bibtex]
Mechanical, thermal transport, electronic and photocatalytic properties of penta-PdPS,-PdPSe and-PdPTe monolayers explored by first-principles calculations (Bohayra Mortazavi, Masoud Shahrokhi, Xiaoying Zhuang, Timon Rabczuk, Alexander V Shapeev), Journal of Materials Chemistry C, Royal Society of Chemistry, volume 10, pages 329–336, 2022. [bibtex]
A machine-learning-based investigation on the mechanical/failure response and thermal conductivity of semiconducting BC2N monolayers (Bohayra Mortazavi, Ivan S Novikov, Alexander V Shapeev), Carbon, Elsevier, volume 188, pages 431–441, 2022. [bibtex]
A first-principles and machine-learning investigation on the electronic, photocatalytic, mechanical and heat conduction properties of nanoporous C 5 N monolayers (Bohayra Mortazavi, Masoud Shahrokhi, Fazel Shojaei, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev), Nanoscale, Royal Society of Chemistry, volume 14, pages 4324–4333, 2022. [bibtex]
Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials (Bohayra Mortazavi, Ali Rajabpour, Xiaoying Zhuang, Timon Rabczuk, Alexander V Shapeev), Carbon, Elsevier, volume 186, pages 501–508, 2022. [bibtex]
Electronic, Optical, Mechanical and Li-Ion Storage Properties of Novel Benzotrithiophene-Based Graphdiyne Monolayers Explored by First Principles and Machine Learning (Bohayra Mortazavi, Fazel Shojaei, Masoud Shahrokhi, Timon Rabczuk, Alexander V Shapeev, Xiaoying Zhuang), Batteries, MDPI, volume 8, pages 194, 2022. [bibtex]
A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides (Bohayra Mortazavi, Fazel Shojaei, Alexander V Shapeev, Xiaoying Zhuang), Carbon, Elsevier, volume 194, pages 230–239, 2022. [bibtex]
Anisotropic mechanical response, high negative thermal expansion, and outstanding dynamical stability of biphenylene monolayer revealed by machine-learning interatomic potentials (Bohayra Mortazavi, Alexander V Shapeev), FlatChem, Elsevier, volume 32, pages 100347, 2022. [bibtex]
Mechanical, optical, and thermoelectric properties of semiconducting ZnIn2X4 (X= S, Se, Te) monolayers (Mohammad Ali Mohebpour, Bohayra Mortazavi, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev, Meysam Bagheri Tagani), Physical Review B, APS, volume 105, pages 134108, 2022. [bibtex]
Constrained Density Functional Theory: a potential-based self-consistency approach (Xavier Gonze, Benjamin Seddon, James A Elliott, Christian Tantardini, Alexander V Shapeev), Journal of Chemical Theory and Computation, ACS Publications, volume 18, pages 6099–6110, 2022. [bibtex]
Short-range order and phase stability of CrCoNi explored with machine learning potentials (Sheuly Ghosh, Vadim Sotskov, Alexander V Shapeev, Jörg Neugebauer, Fritz Körmann), Physical Review Materials, APS, volume 6, pages 113804, 2022. [bibtex]

2021

Machine-learning potentials enable predictive and tractable high-throughput screening of random alloys (Max Hodapp, Alexander Shapeev), Physical Review Materials, APS, volume 5, pages 113802, 2021. [bibtex]
Modeling the high-temperature phase coexistence region of mixed transition metal oxides from ab initio calculations (Suzanne K Wallace, Ambroise van Roekeghem, Anton S Bochkarev, Javier Carrasco, Alexander Shapeev, Natalio Mingo), Physical Review Research, APS, volume 3, pages 013139, 2021. [bibtex]
Free energy of (Co x Mn 1- x)3 O4 mixed phases from machine-learning-enhanced ab initio calculations (Suzanne K Wallace, Anton S Bochkarev, Ambroise van Roekeghem, Javier Carrasco, Alexander Shapeev, Natalio Mingo), Physical Review Materials, APS, volume 5, pages 035402, 2021. [bibtex]
Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass (Evgenii Tsymbalov, Zhe Shi, Ming Dao, Subra Suresh, Ju Li, Alexander Shapeev), npj Computational Materials, Nature Publishing Group, volume 7, pages 1–10, 2021. [bibtex]
Efficient and accurate prediction of elastic properties of Ti0. 5Al0. 5N at elevated temperature using machine learning interatomic potential (Ferenc Tasnádi, Florian Bock, Johan Tidholm, Alexander V Shapeev, Igor A Abrikosov), Thin Solid Films, Elsevier, pages 138927, 2021. [bibtex]
Machine-learned interatomic potentials for alloys and alloy phase diagrams (Conrad W Rosenbrock, Konstantin Gubaev, Alexander V Shapeev, Livia B Pártay, Noam Bernstein, Gábor Csányi, Gus LW Hart), npj Computational Materials, Nature Publishing Group, volume 7, pages 1–9, 2021. [bibtex]
Assessing parameters for ring polymer molecular dynamics simulations at low temperatures: DH+ H chemical reaction (Ivan S Novikov, Yury V Suleimanov, Alexander V Shapeev), Chemical Physics Letters, Elsevier, volume 773, pages 138567, 2021. [bibtex]
Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles (Bohayra Mortazavi, Brahmanandam Javvaji, Fazel Shojaei, Timon Rabczuk, Alexander V Shapeev, Xiaoying Zhuang), Nano Energy, Elsevier, volume 82, pages 105716, 2021. [bibtex]
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, volume 258, pages 107583, 2021. [bibtex]
First-Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine-Learning Interatomic Potentials (Bohayra Mortazavi, Mohammad Silani, Evgeny V Podryabinkin, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev), Advanced Materials, Wiley Online Library, pages 2102807, 2021. [bibtex]
Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams (V. Ladygin, I. Beniya, E. Makarov, A. Shapeev), Physical Review B, APS, volume 104, pages 104102, 2021. [bibtex] [doi]
B2 ordering in body-centered-cubic AlNbTiV refractory high-entropy alloys (Fritz Körmann, Tatiana Kostiuchenko, Alexander Shapeev, Jörg Neugebauer), Physical Review Materials, APS, volume 5, pages 053803, 2021. [bibtex]
Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials (Konstantin Gubaev, Yuji Ikeda, Ferenc Tasnádi, Jörg Neugebauer, Alexander V Shapeev, Blazej Grabowski, Fritz Körmann), Physical Review Materials, APS, volume 5, pages 073801, 2021. [bibtex]
Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials (Konstantin Gubaev, Yuji Ikeda, Ferenc Tasnádi, Jörg Neugebauer, Alexander V Shapeev, Blazej Grabowski, Fritz Körmann), Physical Review Materials, APS, volume 5, pages 073801, 2021. [bibtex]
Ab initio simulations of the surface free energy of TiN (001) (Axel Forslund, Xi Zhang, Blazej Grabowski, Alexander V Shapeev, Andrei V Ruban), Physical Review B, APS, volume 103, pages 195428, 2021. [bibtex]

2020

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]
Metallization of diamond (Zhe Shi, Ming Dao, Evgenii Tsymbalov, Alexander Shapeev, Ju Li, Subra Suresh), Proceedings of the National Academy of Sciences, National Acad Sciences, volume 117, pages 24634–24639, 2020. [bibtex]
High thermal conductivity in semiconducting Janus and non-Janus diamanes (Mostafa Raeisi, Bohayra Mortazavi, Evgeny V Podryabinkin, Fazel Shojaei, Xiaoying Zhuang, Alexander V Shapeev), Carbon, Elsevier, 2020. [bibtex]
The MLIP package: Moment Tensor Potentials with MPI and Active Learning (Ivan S Novikov, Konstantin Gubaev, Evgeny Podryabinkin, Alexander V Shapeev), Machine Learning: Science and Technology, Elsevier, 2020. [bibtex][doi]
Nanoporous C3N4, C3N5 and C3N6 nanosheets; novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties (Bohayra Mortazavi, Fazel Shojaei, Masoud Shahrokhi, Maryam Azizi, Timon Rabczuk, Alexander V Shapeev, Xiaoying Zhuang), Carbon, IOP Publishing, 2020. [bibtex]
Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures (Bohayra Mortazavi, Evgeny V Podryabinkin, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev), Materials Horizons, Royal Society of Chemistry, volume 7, pages 2359–2367, 2020. [bibtex]
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]
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]
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]
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]
Short-range order in face-centered cubic VCoNi alloys (Tatiana Kostiuchenko, Andrei V Ruban, Jörg Neugebauer, Alexander Shapeev, Fritz Körmann), Physical Review Materials, APS, volume 4, pages 113802, 2020. [bibtex]
Ab initio analysis of structural and electronic properties and excitonic optical responses of eight Ge-based 2D materials (Ali Ghojavand, S Javad Hashemifar, Mahdi Tarighi Ahmadpour, Alexander V Shapeev, Amir Alhaji, Qaem Hassanzada), Journal of Applied Physics, AIP Publishing LLC, volume 127, pages 214301, 2020. [bibtex]
Young’s Modulus and Tensile Strength of Ti3C2 MXene Nanosheets As Revealed by In Situ TEM Probing, AFM Nanomechanical Mapping, and Theoretical Calculations (Konstantin L Firestein, Joel E von Treifeldt, Dmitry G Kvashnin, Joseph FS Fernando, Chao Zhang, Alexander G Kvashnin, Evgeny V Podryabinkin, Alexander V Shapeev, Dumindu P Siriwardena, Pavel B Sorokin, others), Nano Letters, ACS Publications, volume 20, pages 5900–5908, 2020. [bibtex]

2019

Ring polymer molecular dynamics and active learning of moment tensor potential for gas-phase barrierless reactions: Application to S + H2 (I.S. Novikov, A.V. Shapeev, Y.V. Suleimanov), Journal of Chemical Physics, v. 151, p. 224105, 2019. [doi]
Accessing thermal conductivity of complex compounds by machine learning interatomic potentials (P. Korotaev, I. Novoselov, A. Yanilkin, A. Shapeev), Physical Review B, APS, v. 100, pages 144308, 2019. [doi]
Ab initio vibrational free energies including anharmonicity for multicomponent alloys (B. Grabowski, Y. Ikeda, P. Srinivasan, F. Körmann, C. Freysoldt, A.I. Duff, A. Shapeev, J. Neugebauer), npj Computational Materials, Nature Publishing Group, v. 5, pages 1-6, 2019. [doi]
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning (E. Tsymbalov, S. Makarychev, Shapeev A., Panov M.), Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Main track. Pages 3599-3605, 2019. [pdf] [doi]
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]
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]
Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials (T. Kostiuchenko, F. Körmann, J. Neugebauer, A. Shapeev), npj Computational Materials, Nature Publishing Group, v. 5, No. 55, 2019. [pdf] [doi] [arXiv]
Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning (E. Podryabinkin, E. Tikhonov, A. Shapeev, A. Oganov), Physical Review B, APS, volume 99, pages 064114, 2019. [bibtex] [pdf] [doi] [arXiv]
Deep elastic strain engineering of bandgap through machine learning (Z. Shi, E. Tsymbalov, M. Dao, S. Suresh, A. Shapeev, J. Li), Proceedings of the National Academy of Sciences , v. 116, p. 4117-4122, 2019.[bibtex] [pdf] [doi]
Machine-learned multi-system surrogate models for materials prediction (G. Nyshadham, M. Rupp, B. Bekker, A. Shapeev, T. Mueller, C. Rosenbrock, G. Csányi, D. Wingate, G. Hart), npj Computational Materials, Nature Publishing Group, v. 5, p. 51, 2019. [bibtex] [pdf] [doi] [arXiv]
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials (K. Gubaev, E. V. Podryabinkin, G. L. W. Hart, A. V. Shapeev), Computational Materials Science, v. 156, p. 148-156, 2019. [doi] [arXiv]
Improving accuracy of interatomic potentials: more physics or more data? A case study of silica (I.Novikov, A.Shapeev), Materials Today Communications, v. 18, p. 74-80, 2019. [doi] [arXiv]
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, v. 165, p. 46–56, 2019. [bibtex] [pdf] [doi] [arXiv]

2018

Automated Calculation of Thermal Rate Coefficients using Ring Polymer Molecular Dynamics and Machine-Learning Interatomic Potentials with Active Learning (I. Novikov, Y. Suleimanov, A. Shapeev), Physical Chemistry Chemical Physics, v. 20, p. 29503-29512, 2018. [doi] [arXiv]
Machine Learning of Molecular Properties: Locality and Active Learning (K. Gubaev, E .V. Podryabinkin, A. V. Shapeev), The Journal of Chemical Physics, volume 148, 241727, 2018. [doi] [arXiv]

2017

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]
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]

2016

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]