4 papers
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…
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…
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…