Work

Prediction of High-Performance Donor–Acceptor Pairs for Organic Photovoltaics with Machine Learning

Year: 2026

Type: article

Abstract: Organic solar cells are widely recognized for their flexibility, light weight and semitransparency, all relevant for niche applications. A significant challenge in their development lies in the accurate prediction of their power conversion efficiency depending on the combination of the donor and acceptor selected in the bulk heterojunction. To address this issue, we developed a robust machine learning (ML) framework designed to establish correlations between molecular structure and device performance. A feature selection strategy, incorporating SHapley Additive exPlanations and Boruta algorithms, was employed to extract the most informative descriptors. Among the regression models that were systematically evaluated on a curated dataset comprising 1575 experimentally characterized donor–acceptor pairs, histogram-based gradient boosting demonstrated superior predictive performance, giving an R2 score of 0.79, with a low root mean square error of 2.16. Subsequently, the optimized model was used to predict new donor–acceptor pairs with PCEs above 20% and identify prospective candidates for further experimental validation.

Source: Solar

DOI: 10.3390/solar6040051

Authors: Esther Mbina, Bruno Grandidier, Kekeli N’Konou

Institutions: Centre National de la Recherche Scientifique, Université de Lille, Institut d'Electronique, de Microélectronique et de Nanotechnologie, Université Polytechnique Hauts-de-France, Centrale Lille Institut

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,207