T
Tsuyoshi Miyazaki
Researcher at National Institute for Materials Science
Publications - 115
Citations - 3412
Tsuyoshi Miyazaki is an academic researcher from National Institute for Materials Science. The author has contributed to research in topics: Density functional theory & Ab initio. The author has an hindex of 26, co-authored 110 publications receiving 2892 citations. Previous affiliations of Tsuyoshi Miyazaki include Tokyo University of Science & University of Tokyo.
Papers
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Journal Article
Atomic force algorithms in DFT electronic-structure techniques based on local orbitals
Journal ArticleDOI
Author Correction: Gate controlling of quantum interference and direct observation of anti-resonances in single molecule charge transport
Yueqi Li,Marius Buerkle,Guangfeng Li,Ali Rostamian,Hui Wang,Zixiao Wang,David R. Bowler,David R. Bowler,Tsuyoshi Miyazaki,Limin Xiang,Yoshihiro Asai,Gang Zhou,Nongjian Tao,Nongjian Tao +13 more
TL;DR: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Book ChapterDOI
Structure of Beryllium Isotopes Beyond the Neutron Dripline
B. Monteagudo,J. Gibelin,F. M. Marqués,A. Corsi,Yuya Kubota,N. A. Orr,G. Authelet,H. Baba,C. Caesar,D. Calvet,A. Delbart,Masanori Dozono,Ji Feng,F. Flavigny,J. M. Gheller,A. Giganon,A. Gillibert,K. Hasegawa,T. Isobe,Y. Kanaya,S. Kawakami,D. Kim,Y. Kiyokawa,Motoki Kobayashi,Nagao Kobayashi,T. Kobayashi,Yosuke Kondo,Z. Korkulu,Shoko Koyama,V. Lapoux,Yukie Maeda,T. Motobayashi,Tsuyoshi Miyazaki,Takashi Nakamura,Noritsugu Nakatsuka,Y. Nishio,A. Obertelli,A. Obertelli,A. Ohkura,S. Ota,Hideaki Otsu,T. Ozaki,V. Panin,Stefanos Paschalis,E. C. Pollacco,S. Reichert,J.-Y. Roussé,Atsumi Saito,Satoshi Sakaguchi,M. Sako,C. Santamaria,Masaki Sasano,H. Sato,M. Shikata,Yohei Shimizu,Y. Shindo,L. Stuhl,Toshiyuki Sumikama,M. Tabata,Yasuhiro Togano,J. Tsubota,T. Uesaka,Zaihong Yang,J. Yasuda,K. Yoneda,Juzo Zenihiro +65 more
TL;DR: In this article, the decay properties of the most neutron-rich Beryllium isotope, a well-known 2n-halo nucleus, have been probed via the proton-knockout reaction from a \(^{17}\)B beam.
Journal ArticleDOI
Machine Learning for Atomic Forces in a Crystalline Solid: Transferability to Various Temperatures
TL;DR: A machine-learning model is trained on a crystalline silicon system to directly predict the atomic forces at a wide range of temperatures using a quantum-mechanical dataset taken from canonical-ensemble simulations at a higher temperature, or an upper bound of the temperature range.
Large-scale ab initio calculations
TL;DR: The CONQUESTcode as discussed by the authors is based on the strategy of minimizing the total energy with respect to the Kohn-Sham density matrix, and the practical techniques for implementing this strategy are outlined.