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.<br><b>[1]</b><i>The ChemFluor_v0.1.zip</i> is the platform based on python, which contain trained models, can be used for the prediction directly.<b>[2]</b><i>Fingerprints_for_prediction.zip</i> is the fingerprints used in our work.<b>[3]</b><i>Materials_Real-World_Problem.zip</i> 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. <b>[4]</b><i>Alldata_SMILES_v0.1.xlsx</i> contains all the molecules in our dataset as well as the references.<br><b>[5]</b>ML-models we used in our paper for real world problems have been saved and uploaded, as<i> model_in_paper.zip.</i><b>[6]</b><i>Molecule-based_partition_withFP.zip </i>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. <i><br></i><i>-----</i><i>update in 2020.07.21</i>[1] QY regressor model have been supported.