Ryosuke Jinnouchi

6.6k total citations · 3 hit papers
81 papers, 5.2k citations indexed

About

Ryosuke Jinnouchi is a scholar working on Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment and Materials Chemistry. According to data from OpenAlex, Ryosuke Jinnouchi has authored 81 papers receiving a total of 5.2k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Electrical and Electronic Engineering, 48 papers in Renewable Energy, Sustainability and the Environment and 42 papers in Materials Chemistry. Recurrent topics in Ryosuke Jinnouchi's work include Electrocatalysts for Energy Conversion (43 papers), Fuel Cells and Related Materials (37 papers) and Machine Learning in Materials Science (28 papers). Ryosuke Jinnouchi is often cited by papers focused on Electrocatalysts for Energy Conversion (43 papers), Fuel Cells and Related Materials (37 papers) and Machine Learning in Materials Science (28 papers). Ryosuke Jinnouchi collaborates with scholars based in Japan, Switzerland and Austria. Ryosuke Jinnouchi's co-authors include Yu Morimoto, Georg Kresse, Ferenc Karsai, Kensaku Kodama, Ryoji Asahi, Alfred B. Anderson, Kenji Kudo, Akira Kuwaki, T. Nagai and Tatsuya Hatanaka and has published in prestigious journals such as Journal of the American Chemical Society, Physical Review Letters and Angewandte Chemie International Edition.

In The Last Decade

Ryosuke Jinnouchi

79 papers receiving 5.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Challenges in applying highly active Pt-based nanostructured catalysts for oxygen reduction reactions to fuel cell vehicles

2021 682 citations Kensaku Kodama, Ryosuke Jinnouchi et al. profile →
On-the-fly machine learning force field generation: Application to melting points

2019 430 citations Ryosuke Jinnouchi, Ferenc Karsai et al. Physical review. B. profile →
Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

2019 415 citations Ryosuke Jinnouchi, Jonathan Lahnsteiner et al. Physical Review Letters profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ryosuke Jinnouchi Japan	31	3.0k	3.0k	2.6k	730	435	81	5.2k
Maria K. Y. Chan United States	39	6.0k 2.0×	4.5k 1.5×	3.9k 1.5×	784 1.1×	509 1.2×	154	9.1k
Zhufeng Hou China	41	3.5k 1.2×	2.2k 0.7×	3.2k 1.2×	191 0.3×	348 0.8×	147	6.1k
Johannes Voss United States	21	2.2k 0.7×	2.0k 0.6×	2.4k 0.9×	223 0.3×	324 0.7×	42	4.4k
Michal Bajdich United States	41	5.1k 1.7×	6.9k 2.3×	3.5k 1.3×	1.7k 2.3×	483 1.1×	88	8.9k
J. M. García‐Lastra Denmark	34	2.4k 0.8×	1.4k 0.5×	2.9k 1.1×	308 0.4×	849 2.0×	125	4.9k
Janis Timoshenko Germany	46	2.4k 0.8×	6.0k 2.0×	4.2k 1.6×	848 1.2×	212 0.5×	140	9.0k
David Loffreda France	32	1.2k 0.4×	2.4k 0.8×	3.3k 1.2×	406 0.6×	981 2.3×	94	5.0k
Nan Xu China	19	2.6k 0.9×	2.2k 0.7×	4.0k 1.5×	234 0.3×	422 1.0×	59	6.4k
Peitao Liu China	34	1.9k 0.6×	2.2k 0.7×	2.1k 0.8×	166 0.2×	436 1.0×	116	4.4k
Tobias Morawietz Germany	28	1.9k 0.6×	1.1k 0.4×	1.1k 0.4×	91 0.1×	410 0.9×	65	2.9k

Countries citing papers authored by Ryosuke Jinnouchi

Since Specialization

Citations

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

Fields of papers citing papers by Ryosuke Jinnouchi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ryosuke Jinnouchi

This figure shows the co-authorship network connecting the top 25 collaborators of Ryosuke Jinnouchi. A scholar is included among the top collaborators of Ryosuke Jinnouchi 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 Ryosuke Jinnouchi. Ryosuke Jinnouchi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Nagai, Tetsuro, et al.. (2025). Machine Learning Model to Predict Free-Energy Landscape and Position-Dependent Diffusion Constant to Extend the Scale of Dynamic Monte Carlo Simulations. Journal of Chemical Theory and Computation. 21(5). 2598–2611. 2 indexed citations

Jinnouchi, Ryosuke & Saori Minami. (2024). The Melamine-Driven Solvation Effect Promotes Oxygen Reduction on a Platinum Catalyst: Machine Learning-Aided Free Energy Calculations. The Journal of Physical Chemistry Letters. 16(1). 265–273. 6 indexed citations

Hijes, Pablo Montero de, Christoph Dellago, Ryosuke Jinnouchi, & Georg Kresse. (2024). Density isobar of water and melting temperature of ice: Assessing common density functionals. The Journal of Chemical Physics. 161(13). 17 indexed citations

Jinnouchi, Ryosuke, Ferenc Karsai, & Georg Kresse. (2024). Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations. Chemical Science. 16(5). 2335–2343. 5 indexed citations

Minami, Saori, et al.. (2024). Impact of Local Water Uptake on Proton Conduction in Covalent Organic Framework Revealed by Machine-Learning Potentials. Chemistry of Materials. 36(19). 9535–9546. 3 indexed citations

Hijes, Pablo Montero de, et al.. (2024). Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks. The Journal of Chemical Physics. 160(11). 26 indexed citations

Kodama, Kensaku, Takahisa Suzuki, Kazuma Shinozaki, & Ryosuke Jinnouchi. (2023). Translating insights from experimental analyses with single-crystal electrodes to practically-applicable material development strategies for controlling the Pt/ionomer interface in polymer electrolyte fuel cells. Journal of Physics Energy. 5(1). 14018–14018. 20 indexed citations

Shinozaki, Kazuma, Shunsuke Yamakawa, Naoki Hasegawa, et al.. (2023). Investigation of gas transport resistance in fuel cell catalyst layers via hydrogen limiting current measurements of CO-covered catalyst surfaces. Journal of Power Sources. 565. 232909–232909. 15 indexed citations

Minami, Saori, et al.. (2023). Measurement of Ionomer Coverage on Carbon and Pt in Catalyst Layer of Polymer Electrolyte Fuel Cells by Electrochemical Impedance Spectroscopy. Electrocatalysis. 14(4). 522–533. 10 indexed citations

10.

Minami, Saori & Ryosuke Jinnouchi. (2023). Accelerating anhydrous proton conduction via anion rotation and hydrogen bond recombination: a machine-learning molecular dynamics. Journal of Materials Chemistry A. 11(30). 16104–16114. 11 indexed citations

11.

Lee, Joohwi & Ryosuke Jinnouchi. (2021). Machine Learning-Based Screening of Highly Stable and Active Ternary Pt Alloys for Oxygen Reduction Reaction. The Journal of Physical Chemistry C. 125(31). 16963–16974. 16 indexed citations

12.

Jinnouchi, Ryosuke, Kenji Kudo, Kensaku Kodama, et al.. (2021). The role of oxygen-permeable ionomer for polymer electrolyte fuel cells. Nature Communications. 12(1). 4956–4956. 206 indexed citations

13.

Beniya, Atsushi, Shougo Higashi, Nobuko Ohba, et al.. (2020). CO oxidation activity of non-reducible oxide-supported mass-selected few-atom Pt single-clusters. Nature Communications. 11(1). 1888–1888. 114 indexed citations

14.

Lahnsteiner, Jonathan, Ryosuke Jinnouchi, & Menno Bokdam. (2019). The essence of long-range order in hybrid perovskites. arXiv (Cornell University). 1 indexed citations

15.

Jinnouchi, Ryosuke, Ferenc Karsai, & Georg Kresse. (2019). On-the-fly machine learning force field generation: Application to melting points. Physical review. B.. 100(1). 430 indexed citations breakdown →

16.

Jinnouchi, Ryosuke, Jonathan Lahnsteiner, Ferenc Karsai, Georg Kresse, & Menno Bokdam. (2019). Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference. Physical Review Letters. 122(22). 225701–225701. 415 indexed citations breakdown →

17.

Kajita, Seiji, Nobuko Ohba, Ryosuke Jinnouchi, & Ryoji Asahi. (2017). A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks. Scientific Reports. 7(1). 16991–16991. 56 indexed citations

18.

Jinnouchi, Ryosuke, Kenji Kudo, Naoki Kitano, & Yu Morimoto. (2015). Molecular Dynamics Simulations on O 2 Permeation through Nafion Ionomer on Platinum Surface. Electrochimica Acta. 188. 767–776. 234 indexed citations

19.

Jinnouchi, Ryosuke, Tetsu Ohsuna, Tatsuya Hatanaka, et al.. (2013). Catalytic Activity of Pt/TaB₂(0001) for the Oxygen Reduction Reaction. Angewandte Chemie International Edition. 52(15). 4137–4140. 31 indexed citations

20.

Jinnouchi, Ryosuke, Kensaku Kodama, Tatsuya Hatanaka, & Yu Morimoto. (2011). First principles based mean field model for oxygen reduction reaction. Physical Chemistry Chemical Physics. 13(47). 21070–21070. 85 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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