Seiji Takeda

208 papers receiving 6.5k citations

Seiji Takeda's Hit Papers

Accelerating materials discovery using artificial intelligence, high performance computing and robotics 2022 · 241 citations
2410+2+4Years since publication4008001.2k

Peers

Seiji Takeda
Comparison fields: 5 of 131
  • Structural Biology 450
  • Catalysis 659
  • Materials Chemistry 3.6k
  • Surfaces, Coatings and Films 423
  • Renewable Energy, Sustainability and the Environment 858
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Apurva Mehta United States
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Citations per year

Countries citing papers authored by Seiji Takeda

Since Specialization
Citations

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

Fields of papers citing papers by Seiji Takeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Seiji Takeda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Seiji Takeda Line = papers co-authored together Seiji Takeda links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 220 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Recent advances in physical reservoir computing: A review
Hit paper breakdown →
20191256
2 2008476
3 2012380
4
Accelerating materials discovery using artificial intelligence, high performance computing and robotics
Hit paper breakdown →
2022241
5 2013184
6 2016182
7 2013158
8 2011155
9 2012135
10 2005112
11 2015110
12 1998108
13 1997107
14 199495
15 201372
16 200971
17 201470
18 199267
19 201767
20 199964

About Seiji Takeda

Seiji Takeda is a scholar working on Materials Chemistry, Electrical and Electronic Engineering, Biomedical Engineering, Atomic and Molecular Physics, and Optics and Surfaces, Coatings and Films, having authored 220 papers that have together received 6.7k indexed citations. Recurring topics across this work include Nanowire Synthesis and Applications (35 papers), Semiconductor materials and interfaces (32 papers), Silicon Nanostructures and Photoluminescence (29 papers), Catalytic Processes in Materials Science (28 papers), Semiconductor materials and devices (22 papers), Silicon and Solar Cell Technologies (21 papers), Electron and X-Ray Spectroscopy Techniques (20 papers) and Advanced Electron Microscopy Techniques and Applications (18 papers). The work is most often cited by research in Structural Biology (450 citations), Catalysis (659 citations), Materials Chemistry (3.6k citations), Surfaces, Coatings and Films (423 citations) and Renewable Energy, Sustainability and the Environment (858 citations). Seiji Takeda has collaborated with scholars based in Japan, United States and China. Frequent co-authors include Hideto Yoshida, Masanori Kohyama, Hideo Kohno, Tetsuya Uchiyama, Daiju Nakano, Yasufumi Kuwauchi, Akira Hirose, Gouhei Tanaka, Naoki Kanazawa and Ryosho Nakane. Their work appears in journals such as Applied Physics Letters, Japanese Journal of Applied Physics, Physical review. B, Condensed matter, Physica B Condensed Matter and Nano 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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