  Accurately predicting the physical and chemical properties of materials
remains one of the most challenging tasks in material design, and one effective
strategy is to construct a reliable data set and use it for training a machine
learning model. In this study, we constructed a large-scale material genome
database (Matgen) containing 76,463 materials collected from
experimentally-observed database, and computed their bandgap properties through
the Density functional theory (DFT) method with Perdew-Burke-Ernzehof (PBE)
functional. We verified the computation method by comparing part of our results
with those from the open Material Project (MP) and Open Quantum Materials
Database (OQMD), all with PBE computations, and found that Matgen achieved the
same computation accuracy based on both measured and computed bandgap
properties. Based on the computed properties of our comprehensive dataset, we
have developed a new graph-based deep learning model, namely CrystalNet,
through our recently developed Communicative Message Passing Neural Network
(CMPNN) framework. The model was shown to outperform other state-of-the-art
prediction models. A further fine-tuning on 1716 experimental bandgap values
(CrystalNet-TL) achieved a superior performance with mean absolute error (MAE)
of 0.77 eV on independent test, which has outperformed the pure PBE (1.14~1.45
eV). Moreover, the model was proven applicable to hypothetical materials with
MAE of 0.77 eV as referred by computations from HSE, a highly accurate quantum
mechanics (QM) method, consist better than PBE (MAE=1.13eV). We also made
material structures, computed properties by PBE, and the CrystalNet models
publically available at https://matgen.nscc-gz.cn.
