Accelerated Discovery of Potential Organic Dyes for Dye‐Sensitized Solar Cells by Interpretable Machine Learning Models and Virtual Screening
Yaping Wen, Lulu Fu, Gongqiang Li, Jing Ma, Haibo Ma
Abstract
The development of highly efficient dye‐sensitized solar cells (DSSCs) is greatly hindered by the lack of a reliable and understandable quantitative structure–property relationship (QSPR) model. Herein, an accurate, robust, and interpretable QSPR model is established by combining the machine learning technique and computational quantum chemistry, and with this model, virtual screening as well as the assessment of synthetic accessibility is performed to identify new efficient and synthetically accessible organic dyes for DSSCs. Finally, eight promising organic dyes with high power conversion efficiency and synthetic accessibility are screened out from ≈10 000 candidates. Meanwhile, the interpretability of the model is used for deducing reasonable chemical rules for high‐performance organic dyes, which are expected to contribute to further innovations for the practical applications of DSSCs.