С. А. Гуда

1.2k total citations
59 papers, 933 citations indexed

About

С. А. Гуда is a scholar working on Materials Chemistry, Radiation and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, С. А. Гуда has authored 59 papers receiving a total of 933 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Materials Chemistry, 12 papers in Radiation and 10 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in С. А. Гуда's work include Machine Learning in Materials Science (23 papers), X-ray Spectroscopy and Fluorescence Analysis (12 papers) and X-ray Diffraction in Crystallography (10 papers). С. А. Гуда is often cited by papers focused on Machine Learning in Materials Science (23 papers), X-ray Spectroscopy and Fluorescence Analysis (12 papers) and X-ray Diffraction in Crystallography (10 papers). С. А. Гуда collaborates with scholars based in Russia, Switzerland and Italy. С. А. Гуда's co-authors include Alexander A. Guda, А. В. Солдатов, Aram L. Bugaev, Carlo Lamberti, Andrea Martini, Mikhail A. Soldatov, Yves Joly, Kirill A. Lomachenko, Grigory Smolentsev and Wojciech Gawełda and has published in prestigious journals such as Journal of the American Chemical Society, Angewandte Chemie International Edition and SHILAP Revista de lepidopterología.

In The Last Decade

С. А. Гуда

54 papers receiving 923 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
С. А. Гуда Russia 15 638 156 150 131 128 59 933
Thomas L. Sheppard Germany 20 782 1.2× 184 1.2× 520 3.5× 161 1.2× 171 1.3× 62 1.1k
T.-C. Weng United States 11 423 0.7× 48 0.3× 263 1.8× 106 0.8× 238 1.9× 18 733
Daniel L. A. Fernandes Sweden 16 672 1.1× 529 3.4× 107 0.7× 45 0.3× 107 0.8× 33 1.0k
Otello Maria Roscioni Italy 17 524 0.8× 93 0.6× 37 0.2× 52 0.4× 176 1.4× 31 1.2k
Hieu‐Chi Dam Japan 22 866 1.4× 136 0.9× 40 0.3× 43 0.3× 77 0.6× 87 1.4k
Yonghao Zhu China 18 515 0.8× 508 3.3× 101 0.7× 77 0.6× 37 0.3× 39 1.2k
Ya Zhuo United States 13 1.3k 2.1× 242 1.6× 90 0.6× 195 1.5× 117 0.9× 14 1.4k
Daxin Shi China 26 575 0.9× 471 3.0× 195 1.3× 153 1.2× 163 1.3× 145 2.0k
Xiaoshuang Li China 22 953 1.5× 126 0.8× 59 0.4× 70 0.5× 106 0.8× 71 1.4k
Tomoya Inoue Japan 19 510 0.8× 112 0.7× 271 1.8× 22 0.2× 111 0.9× 88 949

Countries citing papers authored by С. А. Гуда

Since Specialization
Citations

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

Fields of papers citing papers by С. А. Гуда

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by С. А. Гуда. 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 С. А. Гуда. The network helps show where С. А. Гуда may publish in the future.

Co-authorship network of co-authors of С. А. Гуда

This figure shows the co-authorship network connecting the top 25 collaborators of С. А. Гуда. A scholar is included among the top collaborators of С. А. Гуда 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 С. А. Гуда. С. А. Гуда 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.
Гуда, С. А., D. Trummer, А. В. Солдатов, et al.. (2025). Fingerprint Analysis of X-ray Absorption Spectra with the Machine-Learning Method Trained on the Multielement Experimental Library. The Journal of Physical Chemistry C. 129(5). 2525–2534.
2.
Kubrin, S. P., et al.. (2024). Synthesis, magnetic and structural properties of (1-x)LiFe5O8–(x)LiZn2.5Ti2.5O8 spinel solid solutions. Journal of Alloys and Compounds. 1010. 177205–177205. 1 indexed citations
3.
Власенко, В. Г., С. А. Гуда, И. А. Панкин, et al.. (2024). Improving sensitivity of XANES structural fit to the bridged metal–metal coordination. Journal of Synchrotron Radiation. 31(3). 447–455. 2 indexed citations
4.
5.
Колесников, В. И., et al.. (2024). Nitrogen-Stabilized DLC Coatings: Optimization of Properties and Deposition Parameters Using Randomized Tree and Neural Network Algorithms. Physical Mesomechanics. 27(4). 355–369. 1 indexed citations
6.
Гуда, С. А., et al.. (2023). Fast adaptive sampling with operation time control. Journal of Computational Science. 67. 101946–101946. 2 indexed citations
7.
Nobile, A., D. Trummer, Daniel Klose, et al.. (2023). Active Sites in Cr(III)‐Based Ethylene Polymerization Catalysts from Machine‐Learning‐Supported XAS and EPR Spectroscopy**. Angewandte Chemie. 136(1). 2 indexed citations
8.
Kovalev, D. Yu., et al.. (2023). Sample size dependence of high-temperature thermal stability of Ti2AlN MAX phase. Ceramics International. 49(23). 37912–37921. 5 indexed citations
9.
Nobile, A., D. Trummer, Daniel Klose, et al.. (2023). Active Sites in Cr(III)‐Based Ethylene Polymerization Catalysts from Machine‐Learning‐Supported XAS and EPR Spectroscopy**. Angewandte Chemie International Edition. 63(1). e202313348–e202313348. 5 indexed citations
10.
Гуда, С. А., et al.. (2023). Relationships between synthesis conditions and TiN coating properties discovered from the data driven approach. Thin Solid Films. 768. 139725–139725. 8 indexed citations
11.
Гуда, С. А., et al.. (2023). Structural Phase State of High-Entropy NbTiHfVZr Alloy. The Physics of Metals and Metallography. 124(8). 807–815. 2 indexed citations
12.
Rusalev, Yury V., et al.. (2022). Development of a ReaxFF potential for Au–Pd. Journal of Physics Condensed Matter. 35(6). 65901–65901. 2 indexed citations
13.
Гуда, С. А., et al.. (2022). Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images. Algorithms. 15(11). 398–398. 5 indexed citations
14.
Park, Chang Bae, Aga Shahee, Deepak R. Patil, et al.. (2022). Observation of Spin‐Induced Ferroelectricity in a Layered van der Waals Antiferromagnet CuCrP2S6. Advanced Electronic Materials. 8(6). 34 indexed citations
15.
Martini, Andrea, Aram L. Bugaev, С. А. Гуда, et al.. (2021). Revisiting the Extended X-ray Absorption Fine Structure Fitting Procedure through a Machine Learning-Based Approach. The Journal of Physical Chemistry A. 125(32). 7080–7091. 16 indexed citations
16.
Guda, Alexander A., С. А. Гуда, Andrea Martini, et al.. (2021). Understanding X-ray absorption spectra by means of descriptors and machine learning algorithms. npj Computational Materials. 7(1). 123 indexed citations
17.
Kovalev, D. Yu., et al.. (2020). Density Functional Theory Calculations of the Stability and Statistical Disorder in Crystals of the Kappa Phase of Me3 + xW10 – xC3 + y (Me = Fe, Co, Ni). Russian Journal of Physical Chemistry A. 94(7). 1369–1374. 1 indexed citations
18.
Коновалихин, С. В., et al.. (2019). Estimating the Stability of the Structure of MAX Phases of Ti3AlC2 – хBх Composition on the Basis of Quantum-Chemical Calculations. Russian Journal of Physical Chemistry A. 93(7). 1277–1280. 1 indexed citations
19.
Gorelov, Evgeny, Alexander A. Guda, Mikhail A. Soldatov, et al.. (2018). MLFT approach with p-d hybridization for ab initio simulations of the pre-edge XANES. Radiation Physics and Chemistry. 175. 108105–108105. 6 indexed citations
20.
Guda, Alexander A., И. А. Панкин, Aram L. Bugaev, et al.. (2015). X-ray absorption spectroscopy determination of the products of manganese borohydride decomposition upon heating. Bulletin of the Russian Academy of Sciences Physics. 79(1). 139–143. 6 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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