Curriculum Vitae (English)
Curriculum Vitae (English)
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Education
Education
April 2011 - March 2014: Yokohama Science Frontier High School, Yokohama City
April 2014 - March 2018: Bachelor of Science in Physics, The University of Tokyo
April 2018 - March 2023: Ph.D. Program in Physics, Department of Physics, Graduate School of Science, The University of Tokyo (Sugino Lab, Institute for Solid State Physics)
September 2019 - November 2019: Visiting Researcher, Kieron Burke group, University of California, Irvine (JSPS Young Researcher Overseas Challenge Program for PhD Students)
April 2020: Selected as a Japan Society for the Promotion of Science (JSPS) Research Fellow (DC1)
March 2023: Awarded Ph.D. in Science
Current position
Current position
Preferred Networks Inc., Materials Discovery team researcher
Mainly work on development of atomistic simulation methods
Awards
Awards
March 2020: Research Encouragement Prize, Graduate School of Science, The University of Tokyo
March 2023: Young Scientist Award (Division 11), The Physical Society of Japan
March 2024: Director's Award, ISSP Academic Encouragement Award, Institute for Solid State Physics, The University of Tokyo
Peer-reviewed papers
Peer-reviewed papers
RN, R. Akashi, S Sasaki, and S Tsuneyuki, "Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability", The Journal of chemical physics 148 (24), (2018).
RN, R. Akashi, and O. Sugino, "Completing density functional theory by machine learning hidden messages from molecules", npj Computational Materials, 6, 43 (2020).
Y. Suzuki, RN, and J. Haruyama, "Machine learning exchange-correlation potential in time-dependent density-functional theory", Physical Review A, 101, 050501(2020).
RN, R. Akashi, and O. Sugino, "Machine-learning-based exchange correlation functional with physical asymptotic constraints", Physical Review Research, 4, 013106 (2023).
Reviews and Commentary Articles
Reviews and Commentary Articles
ニューラルネットワーク形式の交換相関ポテンシャル, 「シミュレーション」Vol.39
ニューラルネットワークによる交換相関ポテンシャルの構成, 「アンサンブル」 2019 年 21 巻 2 号
"Machine learning Kohn–Sham exchange–correlation potentials" in Roadmap on machine learning in electronic structure
PFPを用いた高速な結晶構造予測とその効率化アルゴリズムの開発 PFN tech blog (mainly written by the intern)
Invited lectures
Invited lectures
2023.3 The Physical Society of Japan, Young scientis award lecture: "機械学習による交換相関汎関数の構築"
2023.3 DxMT workshop "Machine Learning Based Construction of Density Functional"
2024.3 Spring school of computational physics 2024, "機械学習による原子・電子シミュレーションの精度向上"
Conferences
Conferences
The Physical Society of Japan: many times
American Physical Society March Meeting (2019)
10th Triennial Congress of the International Society for Theoretical Chemical Physic (2019)
Others
Others
April 2020: Selected as a Japan Society for the Promotion of Science (JSPS) Research Fellow (DC1)
October, 2023: HISML2023 organizer
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