Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Atsuto Seko"'
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-7 (2022)
Abstract A recommender system based on experimental databases is useful for the efficient discovery of inorganic compounds. Here, we review studies on the discovery of as-yet-unknown compounds using recommender systems. The first method used composit
Externí odkaz:
https://doaj.org/article/192a78f4af114df487cf28ff516572be
Publikováno v:
Materia Japan. 61:634-639
Publikováno v:
Materia Japan. 61:841-843
Autor:
Atsuto Seko
Publikováno v:
Journal of Applied Physics. 133:011101
Machine learning potentials (MLPs) developed from extensive datasets constructed from density functional theory calculations have become increasingly appealing to many researchers. This paper presents a framework of polynomial-based MLPs, called poly
Autor:
Atsuto Seko, Yudai Iwamizu, Ryoji Kanno, Kosei Ohura, Guowei Zhao, Isao Tanaka, Masaaki Hirayama, Kota Suzuki
Publikováno v:
Journal of Materials Chemistry A. 8:11582-11588
A practical material search using a recommender system is demonstrated to obtain novel lithium ion conducting oxides. The synthesis of unknown chemically relevant compositions (CRCs) proposed by the recommender system and their related materials effe
Publikováno v:
Chemistry of Materials. 31:9984-9992
We propose a machine-learning method to recommend successful processing conditions for new compounds on the basis of parallel experiments. Initially, an experimental database was constructed for 67...
The advanced data structure of the zero-suppressed binary decision diagram (ZDD) enables us to efficiently enumerate nonequivalent substitutional structures. Not only can the ZDD store a vast number of structures in a compressed manner, but also can
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b356542f4dc659e933982733bec1479f
Autor:
Atsuto Seko, Susumu Fujii
In silicon, lattice thermal conductivity plays an important role in a wide range of applications such as thermoelectric and microelectronic devices. Grain boundaries (GBs) in polycrystalline silicon can significantly reduce lattice thermal conductivi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b9464d3160aed28d01a455a27191f2f
Autor:
Atsuto Seko
Publikováno v:
Physical Review B. 102
Machine learning potentials (MLPs) are becoming powerful tools for performing accurate atomistic simulations and crystal structure optimizations. An approach to developing MLPs employs a systematic set of polynomial invariants including high-order on
Autor:
Atsuto Seko, S. Ishiwata
Publikováno v:
Physical Review B. 101
Here, the authors predict the stability of oxygen-deficient perovskite structures in cuprates by density functional theory calculations. They introduce a combination of cluster expansion, Gaussian process, and Bayesian optimization to find stable oxy