ChemFluor

We establish a machine learning-based method to predict emission/absorption wavelength and PLQY of organic fluorescent materials.A platform has been establised for experimenters to use, as well as used for potential high-throughput screening.[1]The ChemFluor_v0.1.zip is the platform based on python, which contain trained models, can be used for the prediction directly.[2]Fingerprints_for_prediction.zip is the fingerprints used in our work.[3]Materials_Real-World_Problem.zip is the molecules collected from recent published work and TD-DFT benchmark studies, which can be seen as real world problem. The molecules are stored in the form of SMILES. [4]Alldata_SMILES_v0.1.xlsx contains all the molecules in our dataset as well as the references.[5]ML-models we used in our paper for real world problems have been saved and uploaded, as model_in_paper.zip.[6]Molecule-based_partition_withFP.zip contain the training set and test set mentioned in our updated manuscript. We split our dataset based on molecules but not data-points in this file. -----update in 2020.07.21[1] QY regressor model have been supported.