| Representation of compounds for machine-learning prediction of physical properties A Seko, H Hayashi, K Nakayama, A Takahashi, I Tanaka Physical Review B 95 (14), 144110, 2017 | 324 | 2017 |
| Sparse representation for a potential energy surface A Seko, A Takahashi, I Tanaka Physical Review B 90 (2), 024101, 2014 | 121 | 2014 |
| First-principles interatomic potentials for ten elemental metals via compressed sensing A Seko, A Takahashi, I Tanaka Physical Review B 92 (5), 054113, 2015 | 99 | 2015 |
| Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: application to elemental titanium A Takahashi, A Seko, I Tanaka Physical Review Materials 1 (6), 063801, 2017 | 74 | 2017 |
| Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations A Takahashi, Y Kumagai, J Miyamoto, Y Mochizuki, F Oba Physical Review Materials 4 (10), 103801, 2020 | 63 | 2020 |
| Insights into oxygen vacancies from high-throughput first-principles calculations Y Kumagai, N Tsunoda, A Takahashi, F Oba Physical Review Materials 5 (12), 123803, 2021 | 54 | 2021 |
| Electrically benign defect behavior in zinc tin nitride revealed from first principles N Tsunoda, Y Kumagai, A Takahashi, F Oba Physical Review Applied 10 (1), 011001, 2018 | 43 | 2018 |
| Theoretical exploration of mixed-anion antiperovskite semiconductors M 3 XN (M= Mg, Ca, Sr, Ba; X= P, As, Sb, Bi) Y Mochizuki, HJ Sung, A Takahashi, Y Kumagai, F Oba Physical Review Materials 4 (4), 044601, 2020 | 37 | 2020 |
| Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power A Takahashi, A Seko, I Tanaka The Journal of chemical physics 148 (23), 2018 | 25 | 2018 |
| Point defects in -type transparent conductive (, Ga, In) from first principles T Gake, Y Kumagai, A Takahashi, F Oba Physical Review Materials 5 (10), 104602, 2021 | 14 | 2021 |
| Origin of large magnetostriction in palladium cobalt and palladium nickel alloys: Strong pseudo-dipole interactions between palladium–cobalt and palladium–nickel atomic pairs T Harumoto, J Shi, Y Nakamura, A Takahashi Applied Physics Letters 118 (10), 2021 | 8 | 2021 |
| Adaptive Sampling Methods via Machine Learning for Materials Screening A Takahashi, Y Kumagai, H Aoki, R Tamura, F Oba Science and Technology of Advanced Materials: Methods 2 (1), 55-66, 2022 | 7 | 2022 |
| Fully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system A Takahashi, K Terayama, Y Kumagai, R Tamura, F Oba Science and Technology of Advanced Materials: Methods 3 (1), 2261834, 2023 | 4 | 2023 |
| Oxygen vacancies in α-(Al x Ga1− x) 2O3 alloys: a first-principles study T Ishii, A Takahashi, T Nagafuji, F Oba Applied Physics Express 16 (6), 061002, 2023 | 3 | 2023 |
| Defect formation and carrier compensation in layered oxychalcogenide La 2 CdO 2 Se 2: an insight from first principles T Gake, Y Kumagai, A Takahashi, H Hiramatsu, F Oba Journal of Materials Chemistry C 10 (44), 16828-16837, 2022 | 2 | 2022 |
| Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds N Sato, A Takahashi, S Kiyohara, K Terayama, R Tamura, F Oba Advanced Intelligent Systems, 2400253, 2024 | 1 | 2024 |
| Carrier generation and compensation mechanism in La 2 SnO 2 S 3 T Nagafuji, K Osuna, K Hanzawa, T Gake, S Bae, Z Hu, T Katase, ... Journal of Materials Chemistry C 12 (31), 12015-12025, 2024 | 1 | 2024 |
| Theoretical and data-driven approaches to semiconductors and dielectrics: from prediction to experiment F Oba, T Nagai, R Katsube, Y Mochizuki, M Tsuji, G Deffrennes, ... Science and Technology of Advanced Materials 25 (1), 2423600, 2024 | | 2024 |
| Dielectric ceramic composition and ceramic capacitor T Murata, H AKAMATSU, F Oba, A Takahashi US Patent App. 17/498,430, 2023 | | 2023 |
