Ryo Tamura

3.1k total citations
117 papers, 2.2k citations indexed

About

Ryo Tamura is a scholar working on Materials Chemistry, Condensed Matter Physics and Computational Theory and Mathematics. According to data from OpenAlex, Ryo Tamura has authored 117 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Materials Chemistry, 20 papers in Condensed Matter Physics and 18 papers in Computational Theory and Mathematics. Recurrent topics in Ryo Tamura's work include Machine Learning in Materials Science (40 papers), Computational Drug Discovery Methods (15 papers) and Theoretical and Computational Physics (15 papers). Ryo Tamura is often cited by papers focused on Machine Learning in Materials Science (40 papers), Computational Drug Discovery Methods (15 papers) and Theoretical and Computational Physics (15 papers). Ryo Tamura collaborates with scholars based in Japan, United Kingdom and France. Ryo Tamura's co-authors include Masaru Tsukada, Koji Tsuda, Kei Terayama, Shu Tanaka, Masato Sumita, Sigeo Ihara, Satoshi Itoh, Kazuto Akagi, Shinsuke Ishihara and Genki Yoshikawa and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Nano Letters.

In The Last Decade

Ryo Tamura

108 papers receiving 2.1k citations

Peers

Ryo Tamura
Maxim Ziatdinov United States
Samuel S. Schoenholz United States
Jun Yuan China
Daniel W. Davies United Kingdom
V.V. Zhirnov United States
Kamal Choudhary United States
Francesca Tavazza United States
Yishai Feldman Israel
In‐Ho Lee South Korea
Lixin Sun United States
Maxim Ziatdinov United States
Ryo Tamura
Citations per year, relative to Ryo Tamura Ryo Tamura (= 1×) peers Maxim Ziatdinov

Countries citing papers authored by Ryo Tamura

Since Specialization
Citations

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

Fields of papers citing papers by Ryo Tamura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ryo Tamura

This figure shows the co-authorship network connecting the top 25 collaborators of Ryo Tamura. A scholar is included among the top collaborators of Ryo Tamura 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 Ryo Tamura. Ryo Tamura 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
1.
Misawa, Takahiro, et al.. (2025). Exploring utilization of generative AI for research and education in data-driven materials science. NIMS Materials Data Repository. 5(1).
2.
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Zhang, Feng, et al.. (2024). Bayesian Optimization for Controlled Chemical Vapor Deposition Growth of WS2. ACS Applied Materials & Interfaces. 16(43). 59109–59115. 3 indexed citations
4.
Urushihara, Makoto, et al.. (2023). Optimization of Core–Shell Nanoparticles Using a Combination of Machine Learning and Ising Machine. SHILAP Revista de lepidopterología. 4(12). 4 indexed citations
5.
Lin, Jianbo, Ryo Tamura, Yasunori Futamura, Tetsuya Sakurai, & Tsuyoshi Miyazaki. (2023). Determination of hyper-parameters in the atomic descriptors for efficient and robust molecular dynamics simulations with machine learning forces. Physical Chemistry Chemical Physics. 25(27). 17978–17986. 4 indexed citations
6.
Zhang, Han, Ryo Tamura, Bo Da, et al.. (2023). Mapping stress inside living cells by atomic force microscopy in response to environmental stimuli. Science and Technology of Advanced Materials. 24(1). 2265434–2265434. 6 indexed citations
7.
Sakaushi, Ken, et al.. (2023). Human–Machine Collaboration for Accelerated Discovery of Promising Oxygen Evolution Electrocatalysts with On-Demand Elements. ACS Central Science. 9(12). 2216–2224. 11 indexed citations
8.
Fujita, T., Kei Terayama, Masato Sumita, et al.. (2022). Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring. Science and Technology of Advanced Materials. 23(1). 352–360. 8 indexed citations
9.
Sumita, Masato, Kei Terayama, Ryo Tamura, & Koji Tsuda. (2022). QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization. Journal of Chemical Information and Modeling. 62(18). 4427–4434. 6 indexed citations
10.
Takahashi, Akira, Yu Kumagai, Hirotaka Aoki, Ryo Tamura, & Fumiyasu Oba. (2022). Adaptive sampling methods via machine learning for materials screening. SHILAP Revista de lepidopterología. 2(1). 55–66. 7 indexed citations
11.
Tanaka, Shu, et al.. (2022). Continuous black-box optimization with an Ising machine and random subspace coding. Physical Review Research. 4(2). 20 indexed citations
12.
Chen, Ta‐Te, Ryo Tamura, Kei Terayama, et al.. (2022). Topological alternation from structurally adaptable to mechanically stable crosslinked polymer. Science and Technology of Advanced Materials. 23(1). 66–75. 9 indexed citations
13.
Tamura, Ryo, Kwangsik Han, Taichi Abe, et al.. (2022). Machine-Learning-Based phase diagram construction for high-throughput batch experiments. SHILAP Revista de lepidopterología. 2(1). 153–161. 9 indexed citations
14.
Sun, Xiaolin, Ryo Tamura, Masato Sumita, et al.. (2021). Integrating Incompatible Assay Data Sets with Deep Preference Learning. ACS Medicinal Chemistry Letters. 13(1). 70–75. 2 indexed citations
15.
Terayama, Kei, Masato Sumita, Ryo Tamura, & Koji Tsuda. (2021). Black-Box Optimization for Automated Discovery. Accounts of Chemical Research. 54(6). 1334–1346. 85 indexed citations
16.
Guo, Jiang, Shenghong Ju, Shu Tanaka, et al.. (2020). Designing metamaterials with quantum annealing and factorization machines. Physical Review Research. 2(1). 114 indexed citations
17.
Sumiya, Masatomo, et al.. (2020). Dynamic Observation and Theoretical Analysis of Initial O2 Molecule Adsorption on Polar and m-Plane Surfaces of GaN. The Journal of Physical Chemistry C. 124(46). 25282–25290. 13 indexed citations
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Sumita, Masato, Ryo Tamura, Kenji Homma, Chioko Kaneta, & Koji Tsuda. (2019). Li-Ion Conductive Li3PO4-Li3BO3-Li2SO4 Mixture: Prevision through Density Functional Molecular Dynamics and Machine Learning. Bulletin of the Chemical Society of Japan. 92(6). 1100–1106. 13 indexed citations
20.
Tamura, Ryo & Koji Hukushima. (2018). Bayesian optimization for computationally extensive probability distributions. PLoS ONE. 13(3). e0193785–e0193785. 13 indexed citations

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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