Qi‐Jun Hong
- Materials Chemistry top 5%
- Mechanical Engineering top 5%
- Catalysis top 5%
- Electrical and Electronic Engineering
- Mechanics of Materials top 10%
- Co-authors
- Axel van de WalleZhi‐Pan LiuQian-Lin TangSergey V. UshakovAlexandra NavrotskySara KadkhodaeiRuoshi SunStefano Curtarolo
- Topics
- Machine Learning in Materials Science (12 papers)High-pressure geophysics and materials (10 papers)Nuclear Materials and Properties (10 papers)
- Journals
- Proceedings of the National Academy of SciencesNature CommunicationsThe Journal of Chemical Physics
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Qi‐Jun Hong
35 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 63
- Materials Chemistry 928
- Mechanical Engineering 405
- Catalysis 238
- Electrical and Electronic Engineering 172
- Mechanics of Materials 140
Countries citing papers authored by Qi‐Jun Hong
This map shows the geographic impact of Qi‐Jun Hong'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 Qi‐Jun Hong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qi‐Jun Hong more than expected).
Fields of papers citing papers by Qi‐Jun Hong
This network shows the impact of papers produced by Qi‐Jun Hong. 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 Qi‐Jun Hong. The network helps show where Qi‐Jun Hong may publish in the future.
Co-authorship network of co-authors of Qi‐Jun Hong
This figure shows the co-authorship network connecting the top 25 collaborators of Qi‐Jun Hong. A scholar is included among the top collaborators of Qi‐Jun Hong 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 Qi‐Jun Hong. Qi‐Jun Hong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 5 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 3 | |
| 10 | 3 | |
| 11 | 6 | |
| 12 | 6 | |
| 13 | Theoretical prediction of high melting temperature for a Mo–Ru–Ta–W HCP multiprincipal element alloybreakdown → | 213 |
| 14 | 1 | |
| 15 | 69 | |
| 16 | 47 | |
| 17 | 45 | |
| 18 | 61 | |
| 19 | 69 | |
| 20 | 195 |
About Qi‐Jun Hong
Qi‐Jun Hong is a scholar working on Geophysics, Materials Chemistry and Atmospheric Science, having authored 39 papers that have together received 1.3k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (12 papers), High-pressure geophysics and materials (10 papers) and Nuclear Materials and Properties (10 papers). The work is most often cited by research in Process Chemistry and Technology (106 citations), Catalysis (238 citations) and Ceramics and Composites (136 citations). Qi‐Jun Hong has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Axel van de Walle, Zhi‐Pan Liu, Qian-Lin Tang, Sergey V. Ushakov, Alexandra Navrotsky, Sara Kadkhodaei, Ruoshi Sun, Stefano Curtarolo, Douglas C. Hofmann and Mark Asta. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and The Journal of Chemical Physics.
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.