Kai Jin

1.6k citations
43 papers · 1.2k indexed · 1 hit paper · h-index 20
Topics
Metal Forming Simulation Techniques (16 papers)Metallurgy and Material Forming (15 papers)Mechanical Behavior of Composites (10 papers)

In The Last Decade

Kai Jin

41 papers receiving 1.2k citations

Hit Papers

Explainable deep learning and virtual evolution identifie...2025202620255101520

Peers

Kai Jin
Comparison fields: 5 of 89
  • Mechanical Engineering 531
  • Mechanics of Materials 422
  • Aerospace Engineering 320
  • Biomedical Engineering 211
  • Materials Chemistry 190
Replace Mario Fafard with:
Mario Fafard Canada
Yao Lu China
Paulo Flores Chile
Marek Opielak Poland
Hussein A.Z. AL-bonsrulah Iraq
Blaža Stojаnović Serbia
Asiful H. Seikh Saudi Arabia
Yuvarajan Devarajan India
Kai Jin relative to Mario Fafard Canada Mario Fafard's profile →
Citations per field
00.5×4.8×
Mario Fafard · 1×
Citations per year

Countries citing papers authored by Kai Jin

Since Specialization
Citations

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

Fields of papers citing papers by Kai Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kai Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Kai Jin. A scholar is included among the top collaborators of Kai Jin 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 Kai Jin. Kai Jin 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
#WorkIndexed citations
1 0
2
Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogensbreakdown →
24
3 2
4 2
5 0
6 5
7 12
8 48
9 8
10 22
11 41
12 30
13 7
14 12
15 22
16 15
17 23
18 2
19 74
20 43

About Kai Jin

Kai Jin is a scholar working on Mechanics of Materials, Mechanical Engineering and Aerospace Engineering, having authored 43 papers that have together received 1.2k indexed citations. Recurring topics across this work include Metal Forming Simulation Techniques (16 papers), Metallurgy and Material Forming (15 papers) and Mechanical Behavior of Composites (10 papers). The work is most often cited by research in Mechanics of Materials (422 citations), Mechanical Engineering (531 citations) and Aerospace Engineering (320 citations). Kai Jin has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Jie Tao, Qing Song, Hua Li, Ying Chen, Hao Wang, Nien‐Hwa Linda Wang, Wan‐Ting Chen, Xunzhong Guo, Naksoo Kim and Jie Tao. Their work appears in journals such as Energy Conversion and Management, Composites Science and Technology and Composites Part B Engineering.

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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