Learning the limits: how data, diversity, and representation control machine-learning predictions of reorganisation energy
Abstract
Accurate and scalable prediction of hole and electron reorganisation energies remains a bottleneck in data-driven design of organic semiconductors, as routine ab initio calculations are impractical for large molecular libraries.
Fields of Study & Subjects
Machine Learning in Materials Science
Organic Electronics and Photovoltaics
Advanced Memory and Neural Computing
Publication details
- Journal / source
- Journal of Materials Chemistry C
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 14
- Issue
- 14
- Pages
- 5999–6011
- Publication date
- 1 January 2026
- Language
- EN