Extrapolating Beyond C60: Advancing Prediction of Fullerene Isomers with FullereneNet

Abstract

Fullerenes, carbon-based nanomaterials with sp²-hybridized carbon atoms arranged in polyhedral cages, exhibit diverse isomeric structures with promising applications in optoelectronics, solar cells, and medicine. However, the vast number of possible fullerene isomers complicates efficient property prediction. In this study, we introduce FullereneNet, a graph neural network-based model that predicts fundamental properties of fullerenes using topological features derived solely from unoptimized structures, eliminating the need for computationally expensive quantum chemistry optimizations. The model leverages topological representations based on the chemical environments of pentagon and hexagon rings, enabling efficient capture of local structural details. We show that this approach yields superior performance in predicting the C–C binding energy for a wide range of fullerene sizes, achieving mean absolute errors of 3 meV/atom for C₆₀, 4 meV/atom for C₇₀, and 6 meV/atom for C₇₂–C₁₀₀, surpassing state-of-the-art machine learning interatomic potentials GAP-20. Additionally, the FullereneNet model accurately predicts 11 other properties, including HOMO-LUMO gap and solvation free energy, demonstrating robustness and transferability across fullerene types. This work provides a computationally efficient framework for high-throughput screening of fullerene candidates and establishes a foundation for future data-driven studies in fullerene chemistry.