Evgeny V. Podryabinkin

2.4k total citations · 3 hit papers
21 papers, 1.7k citations indexed

About

Evgeny V. Podryabinkin is a scholar working on Materials Chemistry, Mechanical Engineering and Biomedical Engineering. According to data from OpenAlex, Evgeny V. Podryabinkin has authored 21 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Materials Chemistry, 7 papers in Mechanical Engineering and 4 papers in Biomedical Engineering. Recurrent topics in Evgeny V. Podryabinkin's work include Machine Learning in Materials Science (10 papers), Thermal properties of materials (3 papers) and Graphene research and applications (3 papers). Evgeny V. Podryabinkin is often cited by papers focused on Machine Learning in Materials Science (10 papers), Thermal properties of materials (3 papers) and Graphene research and applications (3 papers). Evgeny V. Podryabinkin collaborates with scholars based in Russia, United States and Germany. Evgeny V. Podryabinkin's co-authors include Alexander V. Shapeev, Bohayra Mortazavi, Xiaoying Zhuang, Timon Rabczuk, Artem R. Oganov, Evgenii Tikhonov, Mohammad Silani, Ivan S. Novikov, Alexander G. Kvashnin and I.I. Novoselov and has published in prestigious journals such as Advanced Materials, The Journal of Chemical Physics and Nano Letters.

In The Last Decade

Evgeny V. Podryabinkin

21 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Active learning of linearly parametrized interatomic potentials

2017 462 citations Evgeny V. Podryabinkin, Alexander V. Shapeev Computational Materials Science profile →
Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning

2019 280 citations Evgeny V. Podryabinkin, Evgenii Tikhonov et al. Physical review. B. profile →
First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials

2021 263 citations Bohayra Mortazavi, Mohammad Silani et al. Advanced Materials profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Evgeny V. Podryabinkin Russia	13	1.5k	330	195	187	177	21	1.7k
Aria Mansouri Tehrani United States	14	1.1k 0.7×	359 1.1×	154 0.8×	142 0.8×	77 0.4×	27	1.2k
Matthew K. Horton United States	26	1.5k 1.0×	606 1.8×	205 1.1×	112 0.6×	294 1.7×	51	2.1k
Michal Jahnátek Austria	12	1.3k 0.9×	278 0.8×	468 2.4×	108 0.6×	119 0.7×	12	1.6k
Richard Tran United States	12	1.1k 0.7×	415 1.3×	314 1.6×	67 0.4×	138 0.8×	22	1.6k
Lance J. Nelson United States	7	1.0k 0.7×	254 0.8×	187 1.0×	131 0.7×	121 0.7×	8	1.3k
Stefan Müller Germany	20	858 0.6×	210 0.6×	140 0.7×	116 0.6×	202 1.1×	37	1.4k
Tristan Albaret France	15	727 0.5×	186 0.6×	180 0.9×	51 0.3×	160 0.9×	26	1.0k
Pier Luca Palla France	16	1.2k 0.8×	231 0.7×	90 0.5×	43 0.2×	239 1.4×	33	1.6k
Harold T. Stokes United States	9	1.8k 1.2×	583 1.8×	172 0.9×	107 0.6×	124 0.7×	12	2.2k

Countries citing papers authored by Evgeny V. Podryabinkin

Since Specialization

Citations

This map shows the geographic impact of Evgeny V. Podryabinkin'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 Evgeny V. Podryabinkin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Evgeny V. Podryabinkin more than expected).

Fields of papers citing papers by Evgeny V. Podryabinkin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Evgeny V. Podryabinkin. 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 Evgeny V. Podryabinkin. The network helps show where Evgeny V. Podryabinkin may publish in the future.

Co-authorship network of co-authors of Evgeny V. Podryabinkin

This figure shows the co-authorship network connecting the top 25 collaborators of Evgeny V. Podryabinkin. A scholar is included among the top collaborators of Evgeny V. Podryabinkin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Evgeny V. Podryabinkin. Evgeny V. Podryabinkin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Shapeev, Alexander V., et al.. (2024). Exhaustive search for novel multicomponent alloys with brute force and machine learning. npj Computational Materials. 10(1). 2 indexed citations

Podryabinkin, Evgeny V., et al.. (2024). Mechanical Properties of Single and Polycrystalline Solids from Machine Learning. Advanced Theory and Simulations. 7(5). 9 indexed citations

Podryabinkin, Evgeny V., et al.. (2023). MLIP-3: Active learning on atomic environments with moment tensor potentials. The Journal of Chemical Physics. 159(8). 45 indexed citations

Podryabinkin, Evgeny V., et al.. (2023). A machine-learning potential-based generative algorithm for on-lattice crystal structure prediction. Journal of materials research/Pratt's guide to venture capital sources. 38(24). 5161–5170. 6 indexed citations

Podryabinkin, Evgeny V., et al.. (2022). Nanohardness from First Principles with Active Learning on Atomic Environments. Journal of Chemical Theory and Computation. 18(2). 1109–1121. 19 indexed citations

Mortazavi, Bohayra, Mohammad Silani, Evgeny V. Podryabinkin, et al.. (2021). First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials. Advanced Materials. 33(35). e2102807–e2102807. 263 indexed citations breakdown →

Firestein, Konstantin L., Joel E. von Treifeldt, Dmitry G. Kvashnin, et al.. (2020). 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. 20(8). 5900–5908. 167 indexed citations

Mortazavi, Bohayra, Evgeny V. Podryabinkin, Ivan S. Novikov, et al.. (2020). Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials. Journal of Physics Materials. 3(2). 02LT02–02LT02. 68 indexed citations

Mortazavi, Bohayra, Evgeny V. Podryabinkin, Ivan S. Novikov, et al.. (2020). Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution. Computer Physics Communications. 258. 107583–107583. 179 indexed citations

10.

Wang, Qi, et al.. (2020). Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning. npj Computational Materials. 6(1). 41 indexed citations

11.

Mortazavi, Bohayra, et al.. (2020). High thermal conductivity in semiconducting Janus and non-Janus diamanes. Carbon. 167. 51–61. 48 indexed citations

12.

Novoselov, I.I., A. V. Yanilkin, Alexander V. Shapeev, & Evgeny V. Podryabinkin. (2019). Moment tensor potentials as a promising tool to study diffusion processes. Computational Materials Science. 164. 46–56. 75 indexed citations

13.

Podryabinkin, Evgeny V., Evgenii Tikhonov, Alexander V. Shapeev, & Artem R. Oganov. (2019). Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning. Physical review. B.. 99(6). 280 indexed citations breakdown →

14.

Podryabinkin, Evgeny V. & Alexander V. Shapeev. (2017). Active learning of linearly parametrized interatomic potentials. Computational Materials Science. 140. 171–180. 462 indexed citations breakdown →

15.

Гаврилов, А. А., et al.. (2017). Modeling of steady Herschel–Bulkley fluid flow over a sphere. Journal of Engineering Thermophysics. 26(2). 197–215. 24 indexed citations

16.

Podryabinkin, Evgeny V. & Alexander V. Shapeev. (2016). Active learning of linear interatomic potentials. arXiv (Cornell University). 2 indexed citations

17.

Podryabinkin, Evgeny V., et al.. (2015). Modelling of Pressure Fluctuations in a Wellbore While Tripping. 1 indexed citations

18.

Podryabinkin, Evgeny V., et al.. (2014). Evaluation of Pressure Change While Steady-State Tripping. 4 indexed citations

19.

Podryabinkin, Evgeny V., V. Ya. Rudyak, А. А. Гаврилов, & Roland May. (2013). Detailed Modeling of Drilling Fluid Flow in a Wellbore Annulus While Drilling. 13 indexed citations

20.

Podryabinkin, Evgeny V. & V. Ya. Rudyak. (2011). Moment and forces exerted on the inner cylinder in eccentric annular flow. Journal of Engineering Thermophysics. 20(3). 320–328. 12 indexed citations

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.

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