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- Kyoto UniversityJP39 papers
- National Institute for Materials ScienceJP22 papers
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- Centre National de la Recherche ScientifiqueFR4 papers
- National Institute of Advanced Industrial Science and TechnologyJP4 papers
- Japan Synchrotron Radiation Research InstituteJP3 papers
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5 papers · 1 filter
Finding well-optimized special quasirandom structures with decision diagram
Kohei Shinohara, Atsuto Seko, Takashi Horiyama +1
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 s…
Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
Takayuki Nishiyama, Atsuto Seko, Isao Tanaka
Accurate interatomic potentials are in high demand for large-scale atomistic simulations of materials that are prohibitively expensive by density functional theory (DFT) calculatio…
Facilitating {\it ab initio} configurational sampling of multicomponent solids using an on-lattice neural network model and active learning
Shusuke Kasamatsu, Yuichi Motoyama, Kazuyoshi Yoshimi +3
We propose a scheme for {\it ab initio} configurational sampling in multicomponent crystalline solids using Behler-Parinello type neural network potentials (NNPs) in an unconventio…
Derivative structure enumeration using binary decision diagram
Kohei Shinohara, Atsuto Seko, Takashi Horiyama +3
A derivative structure is a nonequivalent substitutional atomic configuration derived from a given primitive cell. The enumeration of derivative structures plays an essential role…
Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential
Atsuto Seko, Atsushi Togo, Isao Tanaka
Many rotational invariants for crystal structure representations have been used to describe the structure-property relationship by machine learning. The machine learning interatomi…