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This study presents a novel Machine Learning Algorithm, named ChemicalEnvironment Graph Neural Network (ChemGNN), designed to accelerate materialsproperty prediction and advance new materials discovery. Graphitic carbonnitride (g-C3N4) and its doped variants have gained significant interest fortheir potential as optical materials. Accurate prediction of their band gaps iscrucial for practical applications, however, traditional quantum simulationmethods are computationally expensive and challenging to explore the vast spaceof possible doped molecular structures. The proposed ChemGNN leverages thelearning ability of current graph neural networks (GNNs) to satisfactorilycapture the characteristics of atoms' local chemical environment underlyingcomplex molecular structures. Our benchmark results demonstrate more than 100%improvement in band gap prediction accuracy over existing GNNs on g-C3N4.Furthermore, the general ChemGNN model can precisely foresee band gaps ofvarious doped g-C3N4 structures, making it a valuable tool for performinghigh-throughput prediction in materials design and development.

Chemical Environment Adaptive Learning for Optical Band Gap Prediction of Doped Graphitic Carbon Nitride Nanosheets