摘要
Organic photovoltaics have attracted worldwide interest due to their unique advantages in developing low-cost,lightweight,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 combining a deep learning model(graph neural network)and an ensemble learning model(Light Gradient Boosting Machine),which enables rapid and accurate screening of organic photovoltaic molecules.This framework establishes the relationship between molecular structure,molecular properties,and device efficiency.Our framework evaluates the chemical structure of the organic photovoltaic molecules directly and accurately.Since it does not involve density functional theory 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.
基金
This work was supported by the National Key Research Program of China(grant No.2022YFA1503101)
Science and Technology Project of Jiangsu Province(grant No.BZ2020011)
National Natural Science Foundation of China(grant No.22173067)
Science and Technology Development Fund,Macao SAR(FDCT No.0052/2021/A)
Collaborative Innovation Center of Suzhou Nano Science&Technology,Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
111 Project,and Joint International Research Laboratory of Carbon-Based Functional Materials and Devices.
