Xingqun He

557 total citations
10 papers, 417 citations indexed

About

Xingqun He is a scholar working on Mechanical Engineering, Materials Chemistry and Aerospace Engineering. According to data from OpenAlex, Xingqun He has authored 10 papers receiving a total of 417 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Mechanical Engineering, 7 papers in Materials Chemistry and 3 papers in Aerospace Engineering. Recurrent topics in Xingqun He's work include Machine Learning in Materials Science (4 papers), Aluminum Alloy Microstructure Properties (3 papers) and Microstructure and mechanical properties (2 papers). Xingqun He is often cited by papers focused on Machine Learning in Materials Science (4 papers), Aluminum Alloy Microstructure Properties (3 papers) and Microstructure and mechanical properties (2 papers). Xingqun He collaborates with scholars based in China, United States and United Kingdom. Xingqun He's co-authors include Huadong Fu, Jianxin Xie, Changsheng Wang, Lei Jiang, Long‐Qing Chen, Hongtao Zhang, Jieqiong Hu, Yongtai Chen, Ming Xie and Jiming Zhang and has published in prestigious journals such as Acta Materialia, Materials Science and Engineering A and Engineering Fracture Mechanics.

In The Last Decade

Xingqun He

10 papers receiving 402 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xingqun He China 6 261 234 129 71 42 10 417
Ruijie Zhang China 13 309 1.2× 305 1.3× 149 1.2× 96 1.4× 30 0.7× 48 478
Hongtao Zhang China 5 356 1.4× 271 1.2× 189 1.5× 105 1.5× 41 1.0× 9 509
Chengxiong Zou China 10 263 1.0× 270 1.2× 89 0.7× 71 1.0× 22 0.5× 23 389
Saif Haider Kayani South Korea 13 283 1.1× 194 0.8× 217 1.7× 69 1.0× 28 0.7× 31 370
Peng Zhu China 12 246 0.9× 185 0.8× 93 0.7× 114 1.6× 31 0.7× 44 455
Bo Cui China 12 200 0.8× 98 0.4× 56 0.4× 56 0.8× 44 1.0× 60 352
Pei Liu China 14 469 1.8× 301 1.3× 133 1.0× 94 1.3× 37 0.9× 35 610
Ryan Schoell United States 9 200 0.8× 187 0.8× 52 0.4× 55 0.8× 33 0.8× 36 312
Tianyi Han China 12 442 1.7× 203 0.9× 249 1.9× 92 1.3× 44 1.0× 24 595

Countries citing papers authored by Xingqun He

Since Specialization
Citations

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

Fields of papers citing papers by Xingqun He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xingqun He

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

All Works

10 of 10 papers shown
1.
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3.
Hu, Jieqiong, et al.. (2023). Solid-liquid phase transition temperature prediction of alloys based on machine learning key feature screening. Applied Materials Today. 36. 102007–102007. 5 indexed citations
4.
Jiang, Lei, Zhihao Zhang, Hao Hu, et al.. (2023). A rapid and effective method for alloy materials design via sample data transfer machine learning. npj Computational Materials. 9(1). 49 indexed citations
5.
Wang, Changsheng, Huadong Fu, Hongtao Zhang, Xingqun He, & Jianxin Xie. (2022). Simultaneous enhancement of mechanical and electrical properties of Cu–Ni–Si alloys via thermo-mechanical process. Materials Science and Engineering A. 838. 142815–142815. 42 indexed citations
6.
Xie, Ming, et al.. (2022). Machine learning accelerates the materials discovery. Materials Today Communications. 33. 104900–104900. 88 indexed citations
7.
Chen, Miaomiao, Qiang Du, Huadong Fu, et al.. (2021). The formation of secondary phase in sub-rapid solidification process of Al-Mg-Si alloys. Materialia. 15. 101022–101022. 6 indexed citations
8.
Zhang, Hongtao, Huadong Fu, Xingqun He, et al.. (2020). Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening. Acta Materialia. 200. 803–810. 215 indexed citations
9.
Zhang, Hongtao, Huadong Fu, Xingqun He, et al.. (2020). Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening. SSRN Electronic Journal. 3 indexed citations
10.
Liu, Xinhua, et al.. (2018). Numerical Simulation Analysis of Continuous Casting Cladding Forming for Cu-Al Composites. Acta Metallurgica Sinica. 54(3). 470–484. 4 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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2026