Kousuke Nakano

2.1k total citations
92 papers, 1.6k citations indexed

About

Kousuke Nakano is a scholar working on Materials Chemistry, Condensed Matter Physics and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, Kousuke Nakano has authored 92 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Materials Chemistry, 26 papers in Condensed Matter Physics and 19 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in Kousuke Nakano's work include Iron-based superconductors research (15 papers), Machine Learning in Materials Science (13 papers) and Advanced Chemical Physics Studies (13 papers). Kousuke Nakano is often cited by papers focused on Iron-based superconductors research (15 papers), Machine Learning in Materials Science (13 papers) and Advanced Chemical Physics Studies (13 papers). Kousuke Nakano collaborates with scholars based in Japan, Italy and France. Kousuke Nakano's co-authors include Hiroshi Kageyama, Ryo Maezono, Kenta Hongo, Takeshi Yajima, Tatsuya Ogino, Yoko Ohtsuka, Yoji Kobayashi, Junri Hattori, Sandro Sorella and Fumitaka Takeiri and has published in prestigious journals such as Journal of the American Chemical Society, Angewandte Chemie International Edition and The Journal of Chemical Physics.

In The Last Decade

Kousuke Nakano

83 papers receiving 1.6k citations

Peers

Kousuke Nakano

CMP

EOMM

EEE

RESE

S. Brennan United States

Hirohiko Sato Japan

Shu Yamaguchi Japan

Stephen McGill United States

Gary Bruno Schmid Germany

Michel Bonnet France

Takashi Nomura Japan

Ting Zhang China

Marie Weinreich Petersen Denmark

M. Akhavan Iran

S. Brennan United States

Kousuke Nakano

713 ×1.1

361 ×0.9

CMP

449 ×1.2

EOMM

442 ×1.7

EEE

107 ×0.6

RESE

Citations per year, relative to Kousuke Nakano Kousuke Nakano (= 1×) peers S. Brennan

Countries citing papers authored by Kousuke Nakano

Since Specialization

Citations

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

Fields of papers citing papers by Kousuke Nakano

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kousuke Nakano

This figure shows the co-authorship network connecting the top 25 collaborators of Kousuke Nakano. A scholar is included among the top collaborators of Kousuke 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 Kousuke Nakano. Kousuke Nakano 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

Nakano, Kousuke, et al.. (2025). Hydrogen liquid-liquid transition from first principles and machine learning. Physical review. B.. 112(10).

Nakano, Kousuke, et al.. (2024). Efficient calculation of unbiased atomic forces in ab initio variational Monte Carlo. Physical review. B.. 109(20). 5 indexed citations

Nakano, Kousuke, et al.. (2024). Principal deuterium Hugoniot via quantum Monte Carlo and Δ-learning. Physical review. B.. 110(4). 5 indexed citations

Hasegawa, Takuya, Jian Xu, Takayuki Nakanishi, et al.. (2024). Key Role of Metal-to-Metal Charge Transfer Transition between Mo⁶⁺ and Bi³⁺ for Enhancement in NIR Luminescence of Gd₂MoO₆:Bi,Yb Nanophosphor. The Journal of Physical Chemistry C. 128(8). 3351–3360. 5 indexed citations

Kato, Daichi, Peng Song, Hiroki Ubukata, et al.. (2023). Evolutionary Algorithm Directed Synthesis of Mixed Anion Compounds LaF₂X (X=Br, I) and LaFI₂. Angewandte Chemie International Edition. 62(30). e202301416–e202301416. 4 indexed citations

Kato, Daichi, Peng Song, Hiroki Ubukata, et al.. (2023). Evolutionary Algorithm Directed Synthesis of Mixed Anion Compounds LaF₂X (X=Br, I) and LaFI₂. Angewandte Chemie. 135(30).

Monacelli, Lorenzo, Michele Casula, Kousuke Nakano, Sandro Sorella, & Francesco Mauri. (2023). Quantum phase diagram of high-pressure hydrogen. Nature Physics. 19(6). 845–850. 36 indexed citations

Maezono, Ryo, et al.. (2023). Toward Chemical Accuracy Using the Jastrow Correlated Antisymmetrized Geminal Power Ansatz. Journal of Chemical Theory and Computation. 19(8). 2222–2229. 9 indexed citations

Nakano, Kousuke, et al.. (2023). TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods. The Journal of Chemical Physics. 159(22). 6 indexed citations

10.

Song, Peng, et al.. (2023). High-pressure phases of BaCN2 explored using a genetic algorithm. Computational Materials Science. 226. 112202–112202. 1 indexed citations

11.

Hongo, Kenta, et al.. (2022). Shry: Application of Canonical Augmentation to the Atomic Substitution Problem. Journal of Chemical Information and Modeling. 62(12). 2909–2915. 10 indexed citations

12.

Ichibha, Tom, et al.. (2022). Diffusion Monte Carlo evaluation of disiloxane linearisation barrier. Physical Chemistry Chemical Physics. 24(6). 3761–3769.

13.

Song, Bin, et al.. (2022). Ab initio molecular dynamics simulation of structural and elastic properties of SiO ₂ –P ₂ O ₅ –Al ₂ O ₃ –Na ₂ O glass. Journal of the American Ceramic Society. 105(11). 6604–6615. 12 indexed citations

14.

Song, Peng, Zhufeng Hou, Kousuke Nakano, et al.. (2022). High-Pressure Mg–Sc–H Phase Diagram and Its Superconductivity from First-Principles Calculations. The Journal of Physical Chemistry C. 126(5). 2747–2755. 29 indexed citations

15.

Ichibha, Tom, Kenta Hongo, Fernando A. Reboredo, et al.. (2022). Diffusion Monte Carlo Study on Relative Stabilities of Boron Nitride Polymorphs. The Journal of Physical Chemistry C. 126(13). 6000–6007. 10 indexed citations

16.

Yano, Masao, et al.. (2022). Feature Space of XRD Patterns Constructed by an Autoencoder. Advanced Theory and Simulations. 6(2). 9 indexed citations

17.

Yoshimoto, Yuki, et al.. (2022). Computational Design to Suppress Thermal Runaway of Li-Ion Batteries via Atomic Substitutions to Cathode Materials. ACS Applied Materials & Interfaces. 14(20). 23355–23363. 5 indexed citations

18.

Hongo, Kenta, et al.. (2021). High-Throughput Evaluation of Discharge Profiles of Nickel Substitution in LiNiO₂ by Ab Initio Calculations. The Journal of Physical Chemistry C. 125(27). 14517–14524. 5 indexed citations

19.

Nakano, Kousuke, Claudio Attaccalite, Luca Capriotti, et al.. (2020). TurboRVB: A many-body toolkit for ab initio electronic simulations by quantum Monte Carlo. The Journal of Chemical Physics. 152(20). 204121–204121. 43 indexed citations

20.

Nakano, Kousuke, Ryo Maezono, & Sandro Sorella. (2020). Speeding up ab initio diffusion Monte Carlo simulations by a smart lattice regularization. Physical review. B.. 101(15). 11 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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