Akira TAKAHASHI
Title
Cited by
Cited by
Year
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
1282017
Sparse representation for a potential energy surface
A Seko, A Takahashi, I Tanaka
Physical Review B 90 (2), 024101, 2014
592014
First-principles interatomic potentials for ten elemental metals via compressed sensing
A Seko, A Takahashi, I Tanaka
Physical Review B 92 (5), 054113, 2015
522015
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
282017
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
112018
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
92018
Theoretical exploration of mixed-anion antiperovskite semiconductors M 3 X N (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
12020
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
2020
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Articles 1–8