The present study totally used 132,180 molecules out of QM9 dataset, and these molecules were randomly partitioned as 88560 and 43620 for the training and testing sets (about 2:1 ratio), respectively. The details of molecule selection are noted in the electronic supplementary information (ESI). All of the molecular structures were previously optimized at B3LYP/6-31G(2df,p) level of theory in the vacuum. These molecules contain up to nine heavy atoms, i.e. carbon, oxygen, nitrogen, and fluorine. The molecular electronic structure properties -E H , E L , and ΔE HL , being predicted at DFT level are adopted as the target properties. The SMILES files and the DFT optimized Cartesian coordinates were used as the input information for the subsequent machine learning and deep learning NN approaches. The energetics distributions of E H , E L , and ΔE HL are schematically shown in Figures . E H appears to be a symmetric distribution among these three properties while E L and ΔE HL generally contains three maximum peaks due to the linear relationship of ΔE HL = E L -E H . In Figure , the sorted E H is plotted with the corresponding E L , and that distribution appears to suggest the independence between E H and E L .