摘要
Integrating artificial intelligence(AI)and computer science together with current approaches in material synthesis and optimization will act as an effective approach for speeding up the discovery of high-performance photoactive materials in organic solar cells(OSCs).Yet,like model selection in statistics,the choice of appropriate machine learning(ML)algorithms plays a vital role in the process of new material discovery in databases.In this study,we constructed five common algorithms,and introduced 565 donor/acceptor(D/A)combinations as training data sets to evaluate the practicalities of these ML algorithms and their application potential when guiding material design and D/A pairs screening.
基金
This work was financially supported by the National Natural Science Foundation of China(NSFC)(grant no.21702154 and 51773157)and the Fundamental Research Funds for the Central Universities
We also thank the support of the opening project of Key Laboratory of Materials Processing and Mold and Beijing National Laboratory for Molecular Sciences(BNLMS201905).
