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Graphitic carbon nitride (g-C<jats:sub>3<\/jats:sub>N<jats:sub>4<\/jats:sub>) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications; however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C<jats:sub>3<\/jats:sub>N<jats:sub>4<\/jats:sub>. 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