1 citations · 4 across the 9 of their papers we have counts for
10 papers
Dynamic Kinetic Evaluation Favors Compositionally Diverse Multicomponent Alloy Nanoparticles for Hydrogen Evolution
Koki Otsuka, Anh Khoa Augustin Lu, Koji Shimizu +1
High-entropy alloy (HEA) nanoparticles offer chemically diverse catalytic sites, but identifying optimal compositions across their large design space remains challenging. We develo…
Adaptable Method for Crystal Design across Diverse Constraints and Objectives with Pretrained Property Predictors
Akihiro Fujii, Yoshitaka Ushiku, Koji Shimizu +2
Advanced crystal design can accelerate materials discovery across applications from photovoltaics to spintronics. Practical design must satisfy multiple properties and physical con…
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…
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…
Enhanced ionic conductivity through crystallization of glass-LiPS by machine learning molecular dynamics simulations
Koji Shimizu, Parth Bahuguna, Shigeo Mori +2
Understanding the atomistic mechanism of ion conduction in solid electrolytes is critical for the advancement of all-solid-state batteries. Glass-ceramics, which undergo crystalliz…
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…