474 citations · 2.3k across the 36 of their papers we have counts for
8 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…
Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics
Susumu Fujii, Atsuto Seko
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 polyc…
Machine learning potentials for multicomponent systems: The Ti-Al binary system
Atsuto Seko
Machine learning potentials (MLPs) are becoming powerful tools for performing accurate atomistic simulations and crystal structure optimizations. An approach to developing MLPs emp…
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…
Machine Learning Potential Repository
Atsuto Seko
This paper introduces a machine learning potential repository that includes Pareto optimal machine learning potentials. It also shows the systematic development of accurate and fas…
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…
Prediction of perovskite-related structures in ACuO (A Ca, Sr, Ba, Sc, Y, La) using density functional theory and Bayesian optimization
Atsuto Seko, Shintaro Ishiwata
Oxygen vacancy ordering in perovskite-type transition-metal oxides plays an important role in the emergence of exotic electronic properties, as typified by superconducting cuprates…
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…