Luckily, advances in artificial intelligence (AI) allow us to bring the simulation cost down and enable investigations of big systems and longer time scale. AI has been instrumental in understanding and discovering interesting mechanistic details in chemical reactions such as roaming. The bottleneck of such AI studies is the construction of robust machine learning potentials and many solutions based on active learning (AL) were suggested to overcome it. For example, AL on small prototypical systems contributed to the understanding that in Diels-Alder reactions, the type of performed dynamics (NVE vs NVT or classical vs ring-polymer MD) has a small effect on the outcome. One of AL solutions is also our recent efficient physics-informed AL protocol which was able to reproduce the earlier quasi-classical MD investigation 3 of the ethene and 1,3-butadiene at UB3LYP/6-31G* in a fraction of computational cost without requiring any HPC. Here, this AL protocol enabled us to investigate the time-resolved mechanism of the Diels-Alder reaction between fullerene C60 and 2,3-dimethyl-1,3-butadiene on a much larger scale than similar state-of-the-art QC investigations (see Computational details). The high quality of the machine learning potential ANI our protocol produced is clearly seen by comparing to DFT static calculations of barrier heights, reaction energies, and IRC shown in Figure . We propagated one thousand forward and backward quasi-classical trajectories with ANI to obtain statistically precise results and extended the propagation time to 1500 fs: this is 10 times longer than in the previous study 3 on the reaction of much smaller ethene and 1,3-butadiene. More trajectories and longer simulations have allowed us to uncover phenomena previously unknown for this reaction, which smaller-scale simulations would have missed. Many studies have reported trajectories of several or dozens picoseconds. Although it is a piece of cake for ML models to run long trajectories, we did not push the propagation that far because of the potential zero-point energy leakage. The zero-point energy of high-frequency modes will flow into low-frequency modes, resulting in faster rotation and even dissociation. Our quasi-classical MD simulations showed that the large size of the C60 and 2,3dimethyl-1,3-butadiene reactants leads to a dynamics behavior in rare trajectories qualitatively different from the cycloaddition reaction of ethene and 1,3-butadiene (Figure ). Unlike the reaction between ethene and 1,3-butadiene where two reactants move closer to each other and form bonds directly without roaming, the bulkier 2,3-dimethyl-1,3-butadiene undergoes a prolonged phase of alignment around the C60 buckyball in 10% of the reactive trajectories (Figure ). During the alignment phase, the diene displays complex 'acrobatic' movements such as somersault (4.6%), twisting (3.9%), twisting somersault (1.4%), and other roaming (0.4%) (Figure , videos of selected trajectories are available on [url upon publication]). We assume that the alignment phase happens if the distances between two bonding carbon atoms and C60 are shorter than 5.0 Å after 1.5 ps; otherwise, the trajectories are deemed to correspond to a direct reaction.