Fast and accurate screening framework for organic solar cells based on molecular structure and deep learning

Abstract

Abstract Organic photovoltaics have attracted worldwide interest due to their unique advantages in developing low-cost, light-weight, and flexible power sources. Functional molecular design and synthesis have been put forward to accelerate the discovery of ideal organic semiconductors, however, it is extremely expensive to conduct experimental screening of the wide organic compound space. Here we develop a framework by combing deep learning model (graph neural network) and ensemble learning model (light Gradient Boosting Machine), which enables rapid and accurate screening of OPV molecules. This framework establishes the relationship between molecular structure, molecular properties, and device efficiency. Our framework evaluates from the chemical structure of the OPV molecules directly and accurately. Since it does not involve DFT calculations, it makes fast predictions. The reliability of our framework is verified with data from previous reports and our newly synthesized organic molecules. Our work provides an efficient method for developing new organic optoelectronic materials.