Ken‐ichi Nomura
- Materials Chemistry top 5%
- Mechanics of Materials top 5%
- Biomedical Engineering top 10%
- Aerospace Engineering top 5%
- Pathology and Forensic Medicine top 10%
- Co-authors
- Aiichiro NakanoPriya VashishtaRajiv K. KaliaMasafumi TaniwakiSubodh TiwariSungwook HongFuyuki ShimojoPankaj Rajak
- Topics
- Machine Learning in Materials Science (21 papers)Lymphoma Diagnosis and Treatment (13 papers)Chronic Lymphocytic Leukemia Research (10 papers)
- Journals
- Journal of the American Chemical SocietyPhysical Review LettersThe Journal of Chemical Physics
- Partner nations
- United StatesJapanThailand
In The Last Decade
Ken‐ichi Nomura
126 papers receiving 2.0k citations
Peers
Comparison fields: 5 of 151
- Materials Chemistry 772
- Mechanics of Materials 421
- Biomedical Engineering 263
- Aerospace Engineering 226
- Pathology and Forensic Medicine 201
Countries citing papers authored by Ken‐ichi Nomura
This map shows the geographic impact of Ken‐ichi Nomura'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 Ken‐ichi Nomura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ken‐ichi Nomura more than expected).
Fields of papers citing papers by Ken‐ichi Nomura
This network shows the impact of papers produced by Ken‐ichi Nomura. 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 Ken‐ichi Nomura. The network helps show where Ken‐ichi Nomura may publish in the future.
Co-authorship network of co-authors of Ken‐ichi Nomura
This figure shows the co-authorship network connecting the top 25 collaborators of Ken‐ichi Nomura. A scholar is included among the top collaborators of Ken‐ichi Nomura 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 Ken‐ichi Nomura. Ken‐ichi Nomura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 2 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 6 | |
| 8 | 5 | |
| 9 | 17 | |
| 10 | 0 | |
| 11 | 5 | |
| 12 | 15 | |
| 13 | 38 | |
| 14 | 12 | |
| 15 | 25 | |
| 16 | 36 | |
| 17 | 12 | |
| 18 | 5 | |
| 19 | 23 | |
| 20 | 116 |
About Ken‐ichi Nomura
Ken‐ichi Nomura is a scholar working on Hardware and Architecture, Materials Chemistry and Genetics, having authored 131 papers that have together received 2.1k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (21 papers), Lymphoma Diagnosis and Treatment (13 papers) and Chronic Lymphocytic Leukemia Research (10 papers). The work is most often cited by research in Ceramics and Composites (101 citations), Mechanics of Materials (421 citations) and Materials Chemistry (772 citations). Ken‐ichi Nomura has collaborated with scholars based in United States, Japan and Thailand. Frequent co-authors include Aiichiro Nakano, Priya Vashishta, Rajiv K. Kalia, Masafumi Taniwaki, Subodh Tiwari, Sungwook Hong, Fuyuki Shimojo, Pankaj Rajak, Shigeo Horiike and Adri C. T. van Duin. Their work appears in journals such as Journal of the American Chemical Society, Physical Review Letters 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.