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40 papers
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…
Phonon structure of titanium under shear deformation along twinning mode
Atsushi Togo, Yuta Inoue, Isao Tanaka
We investigated phonon behavior of hexagonal close packed titanium under homogeneous shear deformation corresponding to the twinning mode using first-principles ca…
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…
Vibrational Effects in X-ray Absorption Spectra of 2D Layered Materials
Weine Olovsson, Teruyasu Mizoguchi, Martin Magnuson +4
With the examples of the C -edge in graphite and the B -edge in hexagonal BN, we demonstrate the impact of vibrational coupling and lattice distortions on the X-ray absorptio…
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…
: a software library for crystal symmetry search
Atsushi Togo, Kohei Shinohara, Isao Tanaka
A computer algorithm to search symmetries of crystal structures as implemented in the \texttt{spglib} code is described. An iterative algorithm is employed to robustly identify spa…