Structure-guided machine learning for efficiency prediction of organic photovoltaics using experimentally informed molecular descriptors
Abstract
Machine learning prediction combining structural descriptors and experimental information accurately models ternary organic solar cells (D1 : D2 : A). An XGBoost model achieves an R 2 of 0.849 for data-driven discovery of organic photovoltaics.
Fields of Study & Subjects
Machine Learning in Materials Science
Computational Drug Discovery Methods
Photovoltaic System Optimization Techniques
Publication details
- Journal / source
- Digital Discovery
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 5
- Issue
- 4
- Pages
- 1510–1521
- Publication date
- 1 January 2026
- Language
- EN