Interpretable AI and Machine Learning Classification for Identifying High-Efficiency Donor-Acceptor Pairs in Organic Solar Cells.

ABSTRACT: To enhance the efficiency of organic solar cells, accurately predicting the efficiency of new pairs of donor and acceptor materials is crucial. Presently, most machine learning studies rely on regression models, which often struggle to establish clear rules for distinguishing between high- and low-performing donor-acceptor pairs. This study proposes a novel approach by integrating interpretable AI, specifically using Shapely values, with four supervised machine learning classification models, namely, support vector machines, decision trees, random forest, and gradient boosting. These models aim to identify high-efficiency donor-acceptor pairs based solely on chemical structures and to extract important features that establish general design principles for distinguishing between high- and l

SUBMITTER: Siddiqui H 

PROVIDER: S-EPMC11325493 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature