49 citations · 79 across the 11 of their papers we have counts for
5 papers · 1 filter
Machine-Learning-based Prediction of Lattice Thermal Conductivity for Half-Heusler Compounds using Atomic Information
Hidetoshi Miyazaki, Tomoyuki Tamura, Masashi Mikami +4
The half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. This is because it has…
Active-learning-based efficient prediction of ab-initio atomic energy: a case study on a Fe random grain boundary model with millions of atoms
Tomoyuki Tamura, Masayuki Karasuyama
We have developed a method that can analyze large random grain boundary (GB) models with the accuracy of density functional theory (DFT) calculations using active learning. It is a…
Knowledge-Transfer based Cost-effective Search for Interface Structures: A Case Study on fcc-Al [110] Tilt Grain Boundary
Tomohiro Yonezu, Tomoyuki Tamura, Ichiro Takeuchi +1
Determining the atomic configuration of an interface is one of the most important issues in materials science research. Although theoretical simulations are effective tools, an exh…
Sparse selection of bases in neural-network potential for crystalline and liquid Si
Ryo Kobayashi, Tomoyuki Tamura, Ichiro Takeuchi +1
The neural-network interatomic potential for crystalline and liquid Si has been developed using the forward stepwise regression technique to reduce the number of bases with keeping…
Static dielectric response and Born effective charge of BN nanotubes from {\it ab initio} finite electric field calculations
G. Y. Guo, S. Ishibashi, T. Tamura +1
{\it Ab initio} investigations of the full static dielectric response and Born effective charge of BN nanotubes (BN-NTs) have been performed for the first time using finite electri…