1 citations · 2 across the 3 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2024
Machine-learning potential for phonon transport in AlN with defects in multiple charge states
Ying Dou, Koji Shimizu, Jesús Carrete +2
Understanding phonon transport properties in defect-laden AlN is important for their device applications. Here, we construct a machine-learning potential to describe phonon transpo…
cond-mat.mtrl-sci2024★ 1 cited
Representing Born effective charges with equivariant graph convolutional neural networks
Alex Kutana, Koji Shimizu, Satoshi Watanabe +1
Graph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-…
cond-mat.mtrl-sci2023★ 1 cited
Prediction of Born effective charges using neural network to study ion migration under electric fields: applications to crystalline and amorphous LiPO
Koji Shimizu, Ryuji Otsuka, Masahiro Hara +2
Understanding ionic behaviour under external electric fields is crucial to develop electronic and energy-related devices using ion transport. In this study, we propose a neural net…