Alexander V. Shapeev

10.5k citations

115 papers · 8.4k · 6 hit papers · h-index 41

Impact in

Materials Chemistry top 0.2%
- Machine Learning in Materials Science
- Thermal properties of materials
- 2D Materials and Applications
- Graphene research and applications
- Boron and Carbon Nanomaterials Research
- Advanced Thermoelectric Materials and Devices
Mechanical Engineering top 2%
- High Entropy Alloys Studies

Papers in

Materials Chemistry 83
- Machine Learning in Materials Science 68
- 2D Materials and Applications 12
- Thermal properties of materials 9
- Boron and Carbon Nanomaterials Research 5
Computational Theory and Mathematics 14
- Advanced Mathematical Modeling in Engineering 14

Co-authors: Evgeny V. Podryabinkin (19 shared papers)Bohayra Mortazavi (20 shared papers)Xiaoying Zhuang (18 shared papers)Timon Rabczuk (14 shared papers)Ivan Sergeyevich Novikov (20 shared papers)Konstantin Gubaev (8 shared papers)Fazel Shojaei (7 shared papers)Fritz Körmann (9 shared papers)
Journals: npj Computational Materials (12 papers)Physical review. B. (11 papers)Computational Materials Science (7 papers)Multiscale Modeling and Simulation (6 papers)Carbon (6 papers)
Partner nations: Russia Germany United States

In The Last Decade

Alexander V. Shapeev

111 papers receiving 8.3k citations

Alexander V. Shapeev's Hit Papers

6 papers align trajectories log scale

First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials 2021 · 303 citations

What are hit papers?

Paper score: A paper's citations measured against the top-1% bar of its own field and year: the square root of citations ÷ bar, so 1 means right at the bar and 2 means four times its citations. The bar blends the citation counts that enter the top 1% of the paper's subfields in its year (weighted 89.5%), of those subfields over all years (0.5%) and of its year across all fields (10%); a subfield and year with fewer than 400 papers takes its year's bar. Papers from 2026 are not scored yet: in an unfinished year, when a paper appeared matters more than how it is cited.

Hit paper: A paper cited at least 1.5× the top-1% bar of its field and year: a paper score of at least √1.5.

2021 Advanced Materials
2020 The Journal of Physical Chemistry A
2020 Nano Energy
2020 Machine Learning Science and Technology
2017 Computational Materials Science
2016 Multiscale Modeling and Simulation

Peers

Countries citing papers authored by Alexander V. Shapeev

Since Specialization

Citations

This map shows the geographic impact of Alexander V. Shapeev's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Alexander V. Shapeev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander V. Shapeev more than expected).

Fields of papers citing papers by Alexander V. Shapeev

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alexander V. Shapeev. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Alexander V. Shapeev. The network helps show where Alexander V. Shapeev may publish in the future.

Co-authors

The 25 scholars most cited alongside Alexander V. Shapeev, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Alexander V. Shapeev Line = papers co-authored together Alexander V. Shapeev links everyone, so they are left out of the graph.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Showing the 20 most-cited of 115 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials Multiscale Modeling and Simulation ·Alexander V. Shapeev Hit paper breakdown →	2016	1230
2	Performance and Cost Assessment of Machine Learning Interatomic Potentials The Journal of Physical Chemistry A ·Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Jörg Behler, Gábor Cśanyi, Alexander V. Shapeev, A. Richard Thompson Hit paper breakdown →	2020	728
3	Active learning of linearly parametrized interatomic potentials Computational Materials Science ·Evgeny V. Podryabinkin, Alexander V. Shapeev Hit paper breakdown →	2017	556
4	Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles Nano Energy ·Bohayra Mortazavi, Brahmanandam Javvaji, Fazel Shojaei, Timon Rabczuk, Alexander V. Shapeev, Xiaoying Zhuang Hit paper breakdown →	2020	533
5	The MLIP package: moment tensor potentials with MPI and active learning Machine Learning Science and Technology ·Ivan Sergeyevich Novikov, Konstantin Gubaev, Evgeny V. Podryabinkin, Alexander V. Shapeev Hit paper breakdown →	2020	482
6	Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning Physical review. B. ·Evgeny V. Podryabinkin, Evgenii Tikhonov, Alexander V. Shapeev, Артем Ромаевич Оганов	2019	338
7	Accelerating high-throughput searches for new alloys with active learning of interatomic potentials Computational Materials Science ·Konstantin Gubaev, Evgeny V. Podryabinkin, Gus L. W. Hart, Alexander V. Shapeev	2018	310
8	First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials Advanced Materials ·Bohayra Mortazavi, Mohammad Silani, Evgeny V. Podryabinkin, Timon Rabczuk, Xiaoying Zhuang, Alexander V. Shapeev Hit paper breakdown →	2021	303
9	Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution Computer Physics Communications ·Bohayra Mortazavi, Evgeny V. Podryabinkin, Ivan Sergeyevich Novikov, Timon Rabczuk, Xiaoying Zhuang, Alexander V. Shapeev	2020	205
10	Young’s Modulus and Tensile Strength of Ti3C2 MXene Nanosheets As Revealed by In Situ TEM Probing, AFM Nanomechanical Mapping, and Theoretical Calculations Nano Letters ·Konstantin L. Firestein, Joel E. von Treifeldt, Dmitry G. Kvashnin, Joseph F. S. Fernando, Chao Zhang, Alexander G. Kvashnin, Evgeny V. Podryabinkin, Alexander V. Shapeev	2020	194
11	Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) ·Bohayra Mortazavi, Ivan Sergeyevich Novikov, Evgeny V. Podryabinkin, Stephan Roche, Timon Rabczuk, Alexander V. Shapeev, Xiaoying Zhuang	2020	179
12	Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) ·Bohayra Mortazavi, Evgeny V. Podryabinkin, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander V. Shapeev	2020	175
13	Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials npj Computational Materials ·Tatiana Kostiuchenko, Fritz Körmann, Jörg Neugebauer, Alexander V. Shapeev	2019	168
14	Nanoporous C3N4, C3N5 and C3N6 nanosheets; novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties Carbon ·Bohayra Mortazavi, Fazel Shojaei, Masoud Shahrokhi, Maryam Azizi, Timon Rabczuk, Alexander V. Shapeev, Xiaoying Zhuang	2020	143
15	Machine-learned multi-system surrogate models for materials prediction npj Computational Materials ·Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker, Alexander V. Shapeev, Tim Mueller, Conrad W. Rosenbrock, Gábor Cśanyi, David Wingate	2019	141
16	Machine-learned interatomic potentials for alloys and alloy phase diagrams npj Computational Materials ·Conrad W. Rosenbrock, Konstantin Gubaev, Alexander V. Shapeev, Lívia B. Pártay, Noam Bernstein, Gábor Cśanyi, Gus L. W. Hart	2021	124
17	Accessing thermal conductivity of complex compounds by machine learning interatomic potentials Physical review. B. ·Pavel Yu. Korotaev, I.I. Novoselov, Alexey V. Yanilkin, Alexander V. Shapeev	2019	122
18	Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials Materials Horizons ·Bohayra Mortazavi, Xiaoying Zhuang, Timon Rabczuk, Alexander V. Shapeev	2023	111
19	Ab initio vibrational free energies including anharmonicity for multicomponent alloys npj Computational Materials ·Blazej Grabowski, Yuji Ikeda, Prashanth Srinivasan, Fritz Körmann, Christoph Freysoldt, Andrew Ian Duff, Alexander V. Shapeev, Jörg Neugebauer	2019	110
20	Deep elastic strain engineering of bandgap through machine learning Proceedings of the National Academy of Sciences ·Zhe Shi, Evgenii Tsymbalov, Ming Dao, S. Suresh, Alexander V. Shapeev, Ju Li	2019	98

About Alexander V. Shapeev

Alexander V. Shapeev is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Mechanical Engineering, Computational Mechanics and Atomic and Molecular Physics, and Optics, having authored 115 papers that have together received 8.4k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (68 papers), Advanced Mathematical Modeling in Engineering (14 papers), 2D Materials and Applications (12 papers), Thermal properties of materials (9 papers), Advanced Chemical Physics Studies (8 papers), High Entropy Alloys Studies (8 papers), Advanced Numerical Methods in Computational Mathematics (6 papers) and Boron and Carbon Nanomaterials Research (5 papers). The work is most often cited by research in Materials Chemistry (6.5k citations), Mechanical Engineering (882 citations), Atomic and Molecular Physics, and Optics (495 citations), Renewable Energy, Sustainability and the Environment (237 citations) and Computational Theory and Mathematics (226 citations). Alexander V. Shapeev has collaborated with scholars based in Russia, Germany and United States. Frequent co-authors include Evgeny V. Podryabinkin, Bohayra Mortazavi, Xiaoying Zhuang, Timon Rabczuk, Ivan Sergeyevich Novikov, Konstantin Gubaev, Fazel Shojaei, Fritz Körmann, Gus L. W. Hart and Gábor Cśanyi. Their work appears in journals such as npj Computational Materials, Physical review. B., Computational Materials Science, Multiscale Modeling and Simulation and Carbon.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact

Alexander V. Shapeev

Impact in

Papers in i

In The Last Decade

Alexander V. Shapeev

Alexander V. Shapeev's Hit Papers

Peers

Countries citing papers authored by Alexander V. Shapeev

Fields of papers citing papers by Alexander V. Shapeev

All Works

About Alexander V. Shapeev

Explore authors with similar magnitude of impact

Papers in