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.

外面功能化富勒烯显示出优于原始碳笼的物理化学性质。官能团可以显着提高溶解度、电子亲和力和光电性能。然而，它们众多的分布模式长期以来一直困扰着理论化学家，并且对于揭示其功能化机制的工具的需求尚未得到满足。这项工作在开源包 AutoSteper 中自动化了传统的逐步模型以及各种分析工作流程。此外，神经网络势（NNP）经过训练、验证，并被证明具有很强的泛化能力和可迁移性。使用 AutoSteper 进行了几个案例研究，以探索功能化机制，例如特定异构体的选择性和功能化途径，以及共结晶系统中的斯通-威尔士重排（SWR）。此外，为了合理缩小巨型纳米团簇的筛选范围，提出了一种新的变体逐步模型。卓有成效的科学发现表明 AutoSteper 能够提供完整、全面且经济高效的分析。通过自动化和NNP的结合，实现了富勒烯化学的数据驱动研究范式。