Predicting power conversion efficiency of binary organic solar cells based on Y6 acceptor by machine learning

Organic solar cells (OSCs) are a promising photovoltaic technology for practical applications. However, the design and synthesis of donor materials molecules based on traditional experimental trial-and-error methods are often complex and expensive in terms of money and time. Machine learning (ML) can effectively learn from data sets and build reliable models to predict the performance of materials with reasonable accuracy. Y6 has become the landmark high-performance OSC acceptor material. We collected the power conversion efficiency (PCE) of small molecular donors and polymer donors based on the Y6 acceptor and calculated their molecule structure descriptors. Then we used six types of algorithms to develop models and compare the predictive performance with the coefficient of determination (R2) and Pearson correlation coefficient (r) as the metrics. Among them, decision tree-based algorithms showed excellent predictive capability, especially the Gradient Boosting Regression Tree (GBRT) models based on small molecular donors and polymer donors exhibited that the values of R2 are 0.84 and 0.69 for the testing set, respectively. Our work provides a strategy to predict PCEs rapidly, and discovers the influence of the descriptors, thereby being expected to screen high-performance donor material molecules.