| Completing density functional theory by machine learning hidden messages from molecules R Nagai, R Akashi, O Sugino npj Computational Materials 6 (1), 43, 2020 | 150 | 2020 |
| Roadmap on machine learning in electronic structure HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure 4 (2), 023004, 2022 | 102 | 2022 |
| Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability R Nagai, R Akashi, S Sasaki, S Tsuneyuki The Journal of chemical physics 148 (24), 2018 | 78 | 2018 |
| Machine-learning-based exchange correlation functional with physical asymptotic constraints R Nagai, R Akashi, O Sugino Physical Review Research 4 (1), 013106, 2022 | 36 | 2022 |
| Machine learning exchange-correlation potential in time-dependent density-functional theory Y Suzuki, R Nagai, J Haruyama Physical Review A 101 (5), 050501, 2020 | 22 | 2020 |
| Completing density functional theory by machine learning hidden messages from molecules. npj Computational Materials 2020, 6, 43 R Nagai, R Akashi, O Sugino DOI, 0 | 6 | |
| Development of exchange-correlation functionals assisted by machine learning R Nagai, R Akashi Machine Learning in Molecular Sciences, 91-112, 2023 | 3 | 2023 |
| Essential difference between the machine learning and artificial Kohn-Sham potentials R Nagai, K Burke, R Akashi, O Sugino Bulletin of the American Physical Society 65, 2020 | | 2020 |
| Neural-Network Implementation of Transferable Kohn-Sham Exchange-Correlation Functionals R Nagai, R Akashi, S Sasaki, S Tsuneyuki, O Sugino APS March Meeting Abstracts 2019, S18. 004, 2019 | | 2019 |
Ryosuke AkashiNational Institutes for Quantum Science and Technology, JapanVerified email at qst.go.jp
