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Improving DFT-predicted band gaps by symbolic regression for semiconductor materials discovery

delete2025-12-24
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PRE
AI
T
Takahiro Kono
S
Souta Miyamoto
T
Taichi Masuda
K
Katsuaki Tanabe *
DOI：10.1016/j.commatsci.2025.114463delete
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Abstract

Abstract

En 中文
• A symbolic regression-based correction function was developed for DFT band gaps. • The correction expanded computational band gap data to over 41,000 entries. • A machine learning model trained on the corrected dataset was constructed. • Compounds with band gaps suitable for solar cell applications were screened. • The workflow could facilitate the discovery of semiconductor materials.

Journal

Computational Materials Science cover
Computational Materials Science
IF:
3.3
Papers:
1.3W
Citations:
3.6W

Organization

K
Kyoto University
Scholars:
5.1W
Papers: 4.6W
Citations: 6.1W
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