1 citations · 3 across the 6 of their papers we have counts for
6 papers
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-…
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints
Akihiro Fujii, Anh Khoa Augustin Lu, Koji Shimizu +1
Materials design aims to discover novel compounds with desired properties. However, prevailing strategies face critical trade-offs. Conventional element-substitution approaches rea…
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…
Enhancing Inverse Problem Solutions with Accurate Surrogate Simulators and Promising Candidates
Akihiro Fujii, Hideki Tsunashima, Yoshihiro Fukuhara +2
Deep-learning inverse techniques have attracted significant attention in recent years. Among them, the neural adjoint (NA) method, which employs a neural network surrogate simulato…
Persistent homology-based descriptor for machine-learning potential of amorphous structures
Emi Minamitani, Ippei Obayashi, Koji Shimizu +1
High-accuracy prediction of the physical properties of amorphous materials is challenging in condensed-matter physics. A promising method to achieve this is machine-learning potent…
Using neural network potential to study point defect properties in multiple charge states of GaN with nitrogen vacancy
Koji Shimizu, Ying Dou, Elvis F. Arguelles +3
Investigation of charged defects is necessary to understand the properties of semiconductors. While density functional theory calculations can accurately describe the relevant phys…