H
Hiori Kino
Researcher at National Institute for Materials Science
Publications - 78
Citations - 2676
Hiori Kino is an academic researcher from National Institute for Materials Science. The author has contributed to research in topics: Density functional theory & Electronic structure. The author has an hindex of 21, co-authored 73 publications receiving 2256 citations.
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Numerical atomic basis orbitals from H to Kr
Taisuke Ozaki,Hiori Kino +1 more
TL;DR: In this article, a systematic study for numerical atomic basis orbitals ranging from H to Kr is presented, which can be used in large scale electronic structure calculations based on density functional theories (DFT).
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Efficient projector expansion for the ab initio LCAO method
Taisuke Ozaki,Hiori Kino +1 more
TL;DR: In this paper, a projector expansion method is presented for an efficient and accurate implementation of the first-principles electronic structure calculations using pseudopotentials and atomic basis functions, by expressing the rapidly varying local potential in the vicinity of nuclei by a separable projector expansion, the difficulty involved in the grid integration using the regular real space grid is remarkably reduced without increasing the computational effort.
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Phase diagram of superconductivity on the anisotropic triangular lattice Hubbard model : An effective model of κ-(BEDT-TTF) salts
Hiori Kino,Hiroshi Kontani +1 more
TL;DR: In this paper, the electronic states of the anisotropic triangular lattice Hubbard model at half-filling were studied for the organic superconducting κ-BEDT-TTF compounds.
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First-principles study of electronic structure in α-(BEDT-TTF)2I3 at ambient pressure and with uniaxial strain
Hiori Kino,Tsuyoshi Miyazaki +1 more
TL;DR: In this paper, the electronic structure of α-(BEDT-TTF) 2 I 3 at 8 K and room temperature at ambient pressure and with uniaxial strain along the...
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Crystal structure prediction accelerated by Bayesian optimization
Tomoki Yamashita,Tomoki Yamashita,Nobuya Sato,Hiori Kino,Takashi Miyake,Takashi Miyake,Koji Tsuda,Koji Tsuda,Tamio Oguchi,Tamio Oguchi +9 more
TL;DR: In this article, a crystal structure prediction method based on Bayesian optimization is proposed, which is classified as a selection-type algorithm which is different from evolution-type algorithms such as an evolutionary algorithm and particle swarm optimization, which can efficiently select the most stable structure from a large number of candidate structures with a lower number of searching trials using a machine learning technique.