Akira TAKAHASHI
TitleCited byYear
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
902017
Sparse representation for a potential energy surface
A Seko, A Takahashi, I Tanaka
Physical Review B 90 (2), 024101, 2014
472014
First-principles interatomic potentials for ten elemental metals via compressed sensing
A Seko, A Takahashi, I Tanaka
Physical Review B 92 (5), 054113, 2015
442015
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
212017
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
62018
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), 234106, 2018
62018
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Articles 1–6