Deep Learning-Driven Prediction of Chemical Addition Patterns for Carboncones and Fullerenes

Carboncones and fullerenes are exemplary π-conjugated carbon nanomaterials with unsaturated, positively curved surfaces, enabling the attachment of atoms or functional groups to enhance their physicochemical properties. However, predicting and understanding the addition patterns in functionalized carboncones and fullerenes are extremely challenging due to the formidable complexity of the regioselectivity exhibited in the adducts. Existing predictive models fall short in systems where the carbon molecular framework undergoes severe distortion upon high degrees of addition. Here, we propose an incremental deep learning approach to predict regioselectivity in the hydrogenation of carboncones and chlorination of fullerenes. Utilizing exclusively graph-based features, our deep neural network (DNN) models rely solely on atomic connectivity, without requiring 3D molecular coordinates as input or iterative optimization of them. This advantage inherently avoids the risk of obtaining chemically unreasonable optimized structures, enabling the handling of highly distorted adducts. The DNN models allow us to study regioselectivity in hydrogenated carboncones of C70H20 and C62H16, accommodating up to at least, 40 and 30 additional H atoms, respectively. Our approach also correctly predicts experimental addition patterns in C50Cl10 and C76Cln (n = 18, 24, and 28), whereas in the latter cases all other known methods have proven unsuccessful. Compared to our previously developed topology-based models, the DNN’s superior predictive power and generalization ability make it a promising tool for investigating complex addition patterns in similar chemical systems.