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

2:15 PM - 2:30 PM JST(5:15 AM - 5:30 AM UTC)

[9p-S301-5] Exploration of Solar Cell Materials Based on Bandgap Correction via Symbolic Regression

〇Takahiro Kono¹, Souta Miyamoto¹, Taichi Masuda¹, Katsuaki Tanabe¹ (1.Kyoto Univ.)

Keywords:

semiconductor,bandgap,machine learning

Using symbolic regression, an equation relating experimentally measured bandgaps to those calculated with densityfunctional theory was derived. Correction values from this equation were employed to construct a machine-learning model that uses physicochemical properties of constituent elements as features. Cu- and Zn-containing ternary compounds were broadly evaluated to narrow down candidates with bandgaps suitable for solar-cell applications. Finally, formation enthalpy was predicted via machine learning to identify materials that are both highly efficient and thermodynamically stable.