I. A. Balyakin

418 total citations
27 papers, 291 citations indexed

About

I. A. Balyakin is a scholar working on Mechanical Engineering, Materials Chemistry and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, I. A. Balyakin has authored 27 papers receiving a total of 291 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Mechanical Engineering, 14 papers in Materials Chemistry and 6 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in I. A. Balyakin's work include High Entropy Alloys Studies (14 papers), Machine Learning in Materials Science (6 papers) and Quantum Dots Synthesis And Properties (5 papers). I. A. Balyakin is often cited by papers focused on High Entropy Alloys Studies (14 papers), Machine Learning in Materials Science (6 papers) and Quantum Dots Synthesis And Properties (5 papers). I. A. Balyakin collaborates with scholars based in Russia and Germany. I. A. Balyakin's co-authors include А. А. Rempel, R. E. Ryltsev, С.А. Упоров, С. И. Садовников, S. V. Rempel, С. Х. Эстемирова, N. M. Chtchelkatchev, V. A. Sidorov, В. А. Быков and Yulia V. Kuznetsova and has published in prestigious journals such as Molecules, Journal of Physics Condensed Matter and Journal of Molecular Liquids.

In The Last Decade

I. A. Balyakin

25 papers receiving 281 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
I. A. Balyakin Russia 10 161 137 89 38 30 27 291
Prashanth Srinivasan Germany 11 178 1.1× 246 1.8× 66 0.7× 37 1.0× 11 0.4× 15 373
Yoav Lederer Germany 4 156 1.0× 131 1.0× 100 1.1× 39 1.0× 13 0.4× 5 259
Ravi S. Patel United States 11 84 0.5× 118 0.9× 85 1.0× 78 2.1× 18 0.6× 26 400
S. Howard United States 10 157 1.0× 78 0.6× 32 0.4× 55 1.4× 11 0.4× 24 275
Y. K. Wu China 8 89 0.6× 317 2.3× 37 0.4× 53 1.4× 11 0.4× 17 440
В. С. Судавцова Ukraine 10 194 1.2× 92 0.7× 30 0.3× 12 0.3× 22 0.7× 80 272
Beatrice Aline Zimmermann Brazil 4 262 1.6× 146 1.1× 48 0.5× 32 0.8× 9 0.3× 7 380
L. D. Son Russia 11 240 1.5× 205 1.5× 28 0.3× 34 0.9× 34 1.1× 50 326
Davide Di Stefano Germany 6 191 1.2× 381 2.8× 28 0.3× 16 0.4× 23 0.8× 9 480
I.I. Novoselov Russia 8 81 0.5× 254 1.9× 27 0.3× 33 0.9× 9 0.3× 12 328

Countries citing papers authored by I. A. Balyakin

Since Specialization
Citations

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

Fields of papers citing papers by I. A. Balyakin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by I. A. Balyakin. 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 I. A. Balyakin. The network helps show where I. A. Balyakin may publish in the future.

Co-authorship network of co-authors of I. A. Balyakin

This figure shows the co-authorship network connecting the top 25 collaborators of I. A. Balyakin. A scholar is included among the top collaborators of I. A. Balyakin 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 I. A. Balyakin. I. A. Balyakin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Упоров, С.А., V. A. Sidorov, N. M. Chtchelkatchev, et al.. (2024). Superior strain gauge sensitivity and elastic anisotropy in TiZrHfTa high entropy alloy. Intermetallics. 177. 108575–108575. 4 indexed citations
2.
Balyakin, I. A., et al.. (2024). Prediction of Mechanical Properties of High-Entropy Carbide (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C with the Use of Machine Learning Potential. Doklady Physical Chemistry. 514(1). 9–14. 5 indexed citations
3.
Balyakin, I. A., et al.. (2024). Neural network molecular dynamics study of LiGe2(PO4)3: Investigation of structure. Computational Materials Science. 239. 112979–112979. 3 indexed citations
4.
Balyakin, I. A., et al.. (2023). MOLECULAR DYNAMICS SIMULATION OF STRATIFICATION IN Bi–Ga MELTS. 406–413.
5.
Balyakin, I. A., R. E. Ryltsev, & N. M. Chtchelkatchev. (2023). Liquid–Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems. Journal of Experimental and Theoretical Physics Letters. 117(5). 370–376. 9 indexed citations
6.
Упоров, С.А., et al.. (2023). Synthesis and magnetic properties of some monotectic composites containing ultra-dispersed particles of YGdTbDyHo high-entropy alloy. Intermetallics. 165. 108121–108121. 3 indexed citations
7.
Упоров, С.А., et al.. (2022). Magnetocaloric effect in ScGdTbDyHo high-entropy alloy: Impact of synthesis route. Intermetallics. 151. 107678–107678. 17 indexed citations
8.
Упоров, С.А., et al.. (2022). Magnetocaloric Effect in ScGdHo Medium-Entropy Alloy. Journal of Superconductivity and Novel Magnetism. 35(6). 1539–1545. 8 indexed citations
9.
Balyakin, I. A., et al.. (2022). Viscosity of liquid gallium: Neural network potential molecular dynamics and experimental study. Computational Materials Science. 215. 111802–111802. 19 indexed citations
10.
Упоров, С.А., R. E. Ryltsev, V. A. Sidorov, et al.. (2021). Pressure effects on electronic structure and electrical conductivity of TiZrHfNb high-entropy alloy. Intermetallics. 140. 107394–107394. 33 indexed citations
11.
Kuznetsova, Yulia V., et al.. (2021). Ag2S interparticle interaction in an aqueous solution: Mechanism of steric and electrostatic stabilization. Journal of Molecular Liquids. 335. 116130–116130. 12 indexed citations
12.
Balyakin, I. A., et al.. (2021). Analysis of the Probability of Synthesizing High-Entropy Alloys in the Systems Ti-Zr-Hf-V-Nb, Gd-Ti-Zr-Nb-Al, and Zr-Hf-V-Nb-Ni. Physical Mesomechanics. 24(6). 701–706. 3 indexed citations
13.
Садовников, С. И. & I. A. Balyakin. (2020). Molecular dynamics simulations of zinc sulfide deposition on silver sulfide from aqueous solution. Computational Materials Science. 184. 109821–109821. 6 indexed citations
14.
Balyakin, I. A., S. V. Rempel, R. E. Ryltsev, & А. А. Rempel. (2020). Deep machine learning interatomic potential for liquid silica. Physical review. E. 102(5). 52125–52125. 38 indexed citations
15.
Balyakin, I. A., et al.. (2020). Ab initio molecular dynamics and high-dimensional neural network potential study of VZrNbHfTa melt. Journal of Physics Condensed Matter. 32(21). 214006–214006. 14 indexed citations
16.
Balyakin, I. A. & С. И. Садовников. (2020). Simulations of ZnS deposition on Ag2S surface and formation of Ag2S/ZnS heteronanostructure. IOP Conference Series Materials Science and Engineering. 1008(1). 12020–12020. 1 indexed citations
17.
Balyakin, I. A. & А. А. Rempel. (2020). Machine learning interatomic potential for molten TiZrHfNb. AIP conference proceedings. 2313. 30037–30037. 4 indexed citations
18.
Balyakin, I. A., et al.. (2019). Importance of Atomic-Like Basis Set Optimization for DFT Modelling of Nanomaterials. Электронный архив ЮУрГУ (South Ural State University). 11(2). 44–50. 1 indexed citations
19.
Balyakin, I. A., Yulia V. Kuznetsova, & А. А. Rempel. (2018). Zeta Potential and Hydrodynamic Radii of Silver Sulfide Nanoparticles in a Colloidal Solution with Mercaptopropylsilane. Russian Journal of Physical Chemistry A. 92(9). 1757–1761. 1 indexed citations
20.
Balyakin, I. A., S. V. Rempel, Yulia V. Kuznetsova, А. В. Сергеев, & А. А. Rempel. (2017). Selforganization of nanoparticles in the system of silver-sulfide-mercaptopropylsilane. AIP conference proceedings. 1885. 20002–20002. 3 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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