| Acceleration of stable interface structure searching using a kriging approach S Kiyohara, H Oda, K Tsuda, T Mizoguchi Japanese Journal of Applied Physics 55 (4), 045502, 2016 | 94 | 2016 |
| Prediction of interface structures and energies via virtual screening S Kiyohara, H Oda, T Miyata, T Mizoguchi Science advances 2 (11), e1600746, 2016 | 93 | 2016 |
| Bayesian optimization for efficient determination of metal oxide grain boundary structures S Kikuchi, H Oda, S Kiyohara, T Mizoguchi Physica B: Condensed Matter 532, 24-28, 2018 | 63 | 2018 |
| Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy S Kiyohara, T Miyata, K Tsuda, T Mizoguchi Scientific reports 8 (1), 13548, 2018 | 57 | 2018 |
| Element-based optimization of waste ceramic materials and glasses recycling I Daigo, S Kiyohara, T Okada, D Okamoto, Y Goto Resources, Conservation and Recycling 133, 375-384, 2018 | 41 | 2018 |
| Transfer learning to accelerate interface structure searches H Oda, S Kiyohara, K Tsuda, T Mizoguchi Journal of the Physical Society of Japan 86 (12), 123601, 2017 | 39 | 2017 |
| Quantitative estimation of properties from core-loss spectrum via neural network S Kiyohara, M Tsubaki, K Liao, T Mizoguchi Journal of Physics: Materials 2 (2), 024003, 2019 | 34 | 2019 |
| Machine learning approaches for ELNES/XANES T Mizoguchi, S Kiyohara Microscopy 69 (2), 92-109, 2020 | 32 | 2020 |
| Machine learning for structure determination and investigating the structure-property relationships of interfaces H Oda, S Kiyohara, T Mizoguchi Journal of Physics: Materials 2 (3), 034005, 2019 | 28 | 2019 |
| Learning excited states from ground states by using an artificial neural network S Kiyohara, M Tsubaki, T Mizoguchi Npj Computational Materials 6 (1), 68, 2020 | 22 | 2020 |
| Effective search for stable segregation configurations at grain boundaries with data-mining techniques S Kiyohara, T Mizoguchi Physica B: Condensed Matter 532, 9-14, 2018 | 22 | 2018 |
| Searching the stable segregation configuration at the grain boundary by a Monte Carlo tree search S Kiyohara, T Mizoguchi The Journal of chemical physics 148 (24), 2018 | 21 | 2018 |
| Radial Distribution Function from X-ray Absorption near Edge Structure with an Artificial Neural Network S Kiyohara, T Mizoguchi Journal of the Physical Society of Japan 89 (10), 103001, 2020 | 17 | 2020 |
| Investigation of segregation of silver at copper grain boundaries by first principles and empirical potential calculations S Kiyohara, T Mizoguchi AIP Conference Proceedings 1763 (1), 2016 | 10 | 2016 |
| Simulated carbon K edge spectral database of organic molecules K Shibata, K Kikumasa, S Kiyohara, T Mizoguchi Scientific data 9 (1), 214, 2022 | 8 | 2022 |
| Nanosized Ti-Based Perovskite Oxides as Acid–Base Bifunctional Catalysts for Cyanosilylation of Carbonyl Compounds T Aihara, W Aoki, S Kiyohara, Y Kumagai, K Kamata, M Hara ACS applied materials & interfaces 15 (14), 17957-17968, 2023 | 6 | 2023 |
| Prediction of interface and vacancy segregation energies at silver interfaces without determining interface structures R Otani, S Kiyohara, K Shibata, T Mizoguchi Applied Physics Express 13 (6), 065504, 2020 | 5 | 2020 |
| Prediction of grain boundary structure and energy by machine learning S Kiyohara, T Miyata, T Mizoguchi arXiv preprint arXiv:1512.03502, 2015 | 4 | 2015 |
| Unique Atomic and Electronic Structures of Oxygen Vacancies in Amorphous SnO2 from First Principles and Informatics S Kiyohara, D Mora-Fonz, A Shluger, Y Kumagai, F Oba The Journal of Physical Chemistry C 126 (44), 18833-18838, 2022 | 3 | 2022 |
| Automatic determination of the spectrum–structure relationship by tree structure-based unsupervised and supervised learning S Kiyohara, K Kikumasa, K Shibata, T Mizoguchi Ultramicroscopy 233, 113438, 2022 | 3 | 2022 |
