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.

[1] QY regressor model have been supported.