[9p-S301-1~12] 23.1 Joint Session N "Informatics"

2:30 PM - 2:45 PM JST(5:30 AM - 5:45 AM UTC)

[9p-S301-6] Exploration of High-Pressure Superconducting Materials in Ternary Hydrides Using Machine Learning

〇Souta Miyamoto¹, Kazuaki Tokuyama¹, Taichi Masuda¹, Katsuaki Tanabe¹ (1.Kyoto Univ.)

Keywords:

superconductor,hydrides,materials informatics

We explored superconducting materials by applying machine-learning techniques to ternary hydrides. A dataset comprising roughly 2,000 records of binary and ternary hydrides was compiled, from which we trained an ensemble of 30 XGBoost models. Using the 95 % lower confidence bound, we ranked potential high-temperature superconductors at 100, 200, and 300 GPa, identifying promising systems outside the training data, such as Ca–Ti–H and Na–Mg–H. Preliminary validation with density-functional theory calculations is currently underway.