Valentin Stanev

1.8k total citations
39 papers, 1.0k citations indexed

About

Valentin Stanev is a scholar working on Condensed Matter Physics, Electronic, Optical and Magnetic Materials and Materials Chemistry. According to data from OpenAlex, Valentin Stanev has authored 39 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Condensed Matter Physics, 13 papers in Electronic, Optical and Magnetic Materials and 10 papers in Materials Chemistry. Recurrent topics in Valentin Stanev's work include Physics of Superconductivity and Magnetism (17 papers), Iron-based superconductors research (12 papers) and Machine Learning in Materials Science (8 papers). Valentin Stanev is often cited by papers focused on Physics of Superconductivity and Magnetism (17 papers), Iron-based superconductors research (12 papers) and Machine Learning in Materials Science (8 papers). Valentin Stanev collaborates with scholars based in United States, Australia and Japan. Valentin Stanev's co-authors include Zlatko Tešanović, Victor Galitski, Ichiro Takeuchi, Jian Kang, A. Gilad Kusne, A. E. Koshelev, C. Broholm, Yusheng Zhao, Sung‐A Chang and Zhiqiang Mao and has published in prestigious journals such as Nature, Physical Review Letters and SHILAP Revista de lepidopterología.

In The Last Decade

Valentin Stanev

37 papers receiving 995 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Valentin Stanev United States 17 595 570 242 174 110 39 1.0k
Jiansheng Wu China 16 481 0.8× 419 0.7× 326 1.3× 471 2.7× 110 1.0× 57 1.0k
D. L. Feng China 16 571 1.0× 782 1.4× 343 1.4× 225 1.3× 263 2.4× 34 1.1k
V. K. Anand India 26 1.7k 2.9× 1.6k 2.8× 312 1.3× 180 1.0× 44 0.4× 124 2.2k
Alireza Akbari Germany 19 461 0.8× 393 0.7× 115 0.5× 321 1.8× 50 0.5× 72 811
Dan Xu China 17 366 0.6× 398 0.7× 279 1.2× 526 3.0× 99 0.9× 41 1.0k
Jiajing Tu China 22 416 0.7× 372 0.7× 133 0.5× 368 2.1× 47 0.4× 96 1.5k
Liang‐Jian Zou China 15 327 0.5× 409 0.7× 537 2.2× 318 1.8× 39 0.4× 91 985
Hiroki Takahashi Japan 19 1.9k 3.2× 2.1k 3.7× 325 1.3× 223 1.3× 524 4.8× 138 2.8k

Countries citing papers authored by Valentin Stanev

Since Specialization
Citations

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

Fields of papers citing papers by Valentin Stanev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Valentin Stanev. 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 Valentin Stanev. The network helps show where Valentin Stanev may publish in the future.

Co-authorship network of co-authors of Valentin Stanev

This figure shows the co-authorship network connecting the top 25 collaborators of Valentin Stanev. A scholar is included among the top collaborators of Valentin Stanev 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 Valentin Stanev. Valentin Stanev 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.
Hutchinson, Mark R., Melissa Damschroder, Valentin Stanev, et al.. (2025). Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning. mAbs. 17(1). 2483944–2483944. 4 indexed citations
2.
Stanev, Valentin, et al.. (2025). Rapid analysis of point-contact Andreev reflection spectra via machine learning with physics-guided data augmentation. Materials Today Physics. 57. 101792–101792.
3.
Zhang, Hua, Jie Yuan, Xianxin Wu, et al.. (2024). The origin of the large $T_{\mathrm{c}}$ variation in FeSe thin films probed by dual-beam pulsed laser deposition. SHILAP Revista de lepidopterología. 3(1). 12–12. 3 indexed citations
4.
Porter, Thomas, Yali Lu, Valentin Stanev, et al.. (2024). Predicting Peptide Ionization Efficiencies for Electrospray Ionization Mass Spectrometry Using Machine Learning. Journal of the American Society for Mass Spectrometry. 35(10). 2297–2307. 2 indexed citations
5.
Takeuchi, Ichiro, et al.. (2023). Predicting the superconducting critical temperature in transition metal carbides and nitrides using machine learning. Physica C Superconductivity. 605. 1354209–1354209. 5 indexed citations
6.
Stanev, Valentin, et al.. (2023). Machine learning modeling of the absorption properties of azobenzene molecules. SHILAP Revista de lepidopterología. 1(1). 100002–100002. 2 indexed citations
7.
Malmquist, Gunnar, et al.. (2023). Exploring features in chromatographic profiles as a tool for monitoring column performance. Journal of Chromatography A. 1698. 463982–463982. 1 indexed citations
8.
Stanev, Valentin, et al.. (2022). Application of machine learning to reflection high-energy electron diffraction images for automated structural phase mapping. Physical Review Materials. 6(6). 17 indexed citations
9.
Takeuchi, Ichiro, et al.. (2022). Predicting the Superconducting Critical Temperature in Transition Metal Carbides and Nitrides Using Machine Learning. SSRN Electronic Journal. 1 indexed citations
10.
Stanev, Valentin, Kamal Choudhary, A. Gilad Kusne, Johnpierre Paglione, & Ichiro Takeuchi. (2021). Artificial intelligence for search and discovery of quantum materials. Communications Materials. 2(1). 48 indexed citations
11.
Lee, Seunghun, Valentin Stanev, Xiaohang Zhang, et al.. (2019). Perfect Andreev reflection due to the Klein paradox in a topological superconducting state. Nature. 570(7761). 344–348. 37 indexed citations
12.
Iwasaki, Yuma, Ichiro Takeuchi, Valentin Stanev, et al.. (2019). Machine-learning guided discovery of a new thermoelectric material. Scientific Reports. 9(1). 2751–2751. 95 indexed citations
13.
Alexandrov, Boian S., Valentin Stanev, Velimir V. Vesselinov, & K. Ø. Rasmussen. (2019). Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers. Statistical Analysis and Data Mining The ASA Data Science Journal. 12(4). 302–310. 8 indexed citations
14.
Stanev, Valentin, et al.. (2018). Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals. PLoS ONE. 13(3). e0193974–e0193974. 7 indexed citations
15.
Stanev, Valentin & A. E. Koshelev. (2014). Complex state induced by impurities in multiband superconductors. Physical Review B. 89(10). 18 indexed citations
16.
Stanev, Valentin & P. B. Littlewood. (2013). Nematicity driven by hybridization in iron-based superconductors. Physical Review B. 87(16). 25 indexed citations
17.
Chaparro, Carlos, H. Claus, A. Rydh, et al.. (2012). BaFe 2 (As 1-x P x ) 2 単結晶の比熱のドーピング依存性. Physical Review B. 85(18). 1–184525. 9 indexed citations
18.
Alexandrov, Boian S., Valentin Stanev, A. R. Bishop, & K. Ø. Rasmussen. (2012). Anharmonic dynamics of intramolecular hydrogen bonds driven by DNA breathing. Physical Review E. 86(6). 61913–61913. 2 indexed citations
19.
Vakaryuk, Victor, et al.. (2012). Topological Defect-Phase Soliton and the Pairing Symmetry of a Two-Band Superconductor: Role of the Proximity Effect. Physical Review Letters. 109(22). 227003–227003. 23 indexed citations
20.
Bao, Wei, Yusheng Zhao, C. Broholm, et al.. (2009). Spin Gap and Resonance at the Nesting Wave Vector in SuperconductingFeSe0.4Te0.6. Physical Review Letters. 103(6). 67008–67008. 178 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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