Aiichiro Nakano

13.1k citations
398 papers · 10.1k indexed · h-index 55
Topics
Machine Learning in Materials Science (44 papers)2D Materials and Applications (37 papers)Advanced Chemical Physics Studies (37 papers)
Partner nations
United StatesJapanBrazil

In The Last Decade

Aiichiro Nakano

389 papers receiving 9.9k citations

Peers

Aiichiro Nakano
Comparison fields: 5 of 143
  • Materials Chemistry 6.2k
  • Electrical and Electronic Engineering 2.4k
  • Mechanics of Materials 2.2k
  • Atomic and Molecular Physics, and Optics 2.0k
  • Ceramics and Composites 1.6k
Replace Rajiv K. Kalia with:
Rajiv K. Kalia United States
Priya Vashishta United States
Hasan Metin Aktulga United States
Aidan P. Thompson United States
Christian Robert Trott United States
Paul Crozier United States
Trung Dac Nguyen United States
Axel Kohlmeyer United States
Fuyuki Shimojo Japan
Martin T. Dove United Kingdom
Aiichiro Nakano relative to Rajiv K. Kalia United States Rajiv K. Kalia's profile →
Citations per field
00.5×1.5×
Rajiv K. Kalia · 1×
Citations per year

Countries citing papers authored by Aiichiro Nakano

Since Specialization
Citations

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

Fields of papers citing papers by Aiichiro Nakano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aiichiro Nakano

This figure shows the co-authorship network connecting the top 25 collaborators of Aiichiro Nakano. A scholar is included among the top collaborators of Aiichiro Nakano 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 Aiichiro Nakano. Aiichiro Nakano 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 4
2 2
3 3
4 2
5 2
6 2
7 1
8 5
9 16
10 15
11 36
12 12
13 24
14 13
15 47
16 12
17 10
18 60
19 34
20 3

About Aiichiro Nakano

Aiichiro Nakano is a scholar working on Ceramics and Composites, Materials Chemistry and Hardware and Architecture, having authored 398 papers that have together received 10.1k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (44 papers), 2D Materials and Applications (37 papers) and Advanced Chemical Physics Studies (37 papers). The work is most often cited by research in Ceramics and Composites (1.6k citations), Materials Chemistry (6.2k citations) and Mechanics of Materials (2.2k citations). Aiichiro Nakano has collaborated with scholars based in United States, Japan and Brazil. Frequent co-authors include Priya Vashishta, Rajiv K. Kalia, Fuyuki Shimojo, José Pedro Rino, Ken‐ichi Nomura, Shūji Ogata, Izabela Szlufarska, Ingvar Ebbsjö, Timothy J. Campbell and Aravind Krishnamoorthy. Their work appears in journals such as Science, Journal of the American Chemical Society and Physical Review Letters.

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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