Exploring exohedral functionalization of fullerene with automation and Neural Network Potential

Exohedral functionalized fullerenes have shown superior physicochemical properties over pristine carbon cages. The functional groups could significantly improve solubility, electron affinity, and photoelectric properties. However, their numerous distribution patterns have long been puzzling for theoretical chemists, and there are unmet needs for tools to unveil their functionalization mechanism. This work automates the traditional stepwise model as well as various analysis workflows within an open-source package AutoSteper. Besides, a Neural Network Potential (NNP) is trained, validated, and proven to have great generalization ability and transferability. Several case studies are performed with AutoSteper to explore functionalization mechanisms, such as the selectivity and the functionalization pathway of a specific isomer, and the Stone-Wales Rearrangement (SWR) in co-crystallization systems. In addition, to reasonably narrow down the screen scope for giant nanoclusters, a new variant stepwise model is proposed. Fruitful scientific discoveries demonstrate that AutoSteper is capable of providing complete, comprehensive, and cost-effective analysis. With the combination of Automation and NNP, a data-driven research paradigm is realized for fullerene chemistry.