摘要
It is a time-consuming and costly process to develop affordable and high-performance organic photovoltaic materials.Computational methods are essential for accelerating the material discovery process by predicting power conversion efficiencies(PCE).In this study,we propose a deep learningbased framework(DeepAcceptor)to design and discover highly efficient small molecule acceptor materials.Specifically,an experimental dataset is constructed by collecting acceptor data from publications.Then,a deep learning-based model is customized to predict PCEs by applying graph representation learning to Bidirectional Encoder Representations from Transformers(BERT),with the atom,bond,and connection information in acceptor molecular structures as the input(abcBERT).
基金
the National Natural Science Foundation of China grants 22273120,21873116,and 22373117.
