6 citations · 6 across the 4 of their papers we have counts for
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Machine Learning Approaches to Point Defects in Non-Metallic Materials: A Review of Methods
Yu Kumagai, Shin Kiyohara
We review recent machine-learning (ML) approaches for point defects in non-metallic materials, with an emphasis on defect formation energies. Existing studies largely fall into two…
Charting the Landscape of Oxygen Ion Conductors: A 60-Year Dataset with Interpretable Regression Models
Seong-Hoon Jang, Shin Kiyohara, Hitoshi Takamura +1
Oxygen ion conductors are indispensable materials for such as solid oxide fuel cells, sensors, and membranes. Despite extensive research across diverse structural families, systema…
Machine Learning Prediction of Charged Defect Formation Energies from Crystal Structures
Shin Kiyohara, Chisa Shibui, Soungmin Bae +1
Recent advances in materials informatics have expanded the number of synthesizable materials. However, screening promising candidates, such as semiconductors, based on defect prope…
Physics-Based Factorized Machine Learning for Predicting Ionic Dielectric Tensors
Atsushi Takigawa, Shin Kiyohara, Yu Kumagai
Considerable effort continues to be devoted to the exploration of next-generation high-\k{appa} materials that combine a high dielectric constant with a wide band gap. However, mac…