Data-driven machine learning models for quick prediction of the Stokes shift of organic fluorescent materials

Organic fluorescent materials are widely used in various fields, including OLEDs, organic solar cells, and bio-imaging. However, designing and synthesizing new fluorescent organic materials with desirable properties for specific applications require knowledge of the chemical and physical properties of previously studied molecules. One critical property of fluorescent organic compounds is the Stokes shift, which is usually measured experimentally and is known to be time-consuming. Time-dependent density functional theory (TD-DFT) has been used to predict Stokes shifts, but its computational costs restrict the screening of fluorescent organic materials. To address this challenge, we propose a machine learning model based on an ensemble learning approach called LightGBM algorithm, to predict the Stokes shift of organic fluorescent compounds. Based on 15,987 sets of Stokes shift data processed with molecular fingerprints, our ML models show satisfactory results. The squared correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) of the independent test set for the optimal model are 0.86, 12.27 nm, and 19.16 nm, respectively. Unseen cases confirmed the prediction performance of our ML model. Finally, we applied the ML prediction model to enable rapid screening of organic fluorescent compound with desired Stokes shift. Our study presents a rapid and accurate method for predicting the Stokes shift of organic fluorescent compounds, which accelerate the design of organic fluorescent materials with desired Stokes shift. All source codes and dataset are freely available at https://github.com/Yihuan-Zhao93/Stocks-shiftsML.