Surprising Dynamics Phenomena in the Diels–Alder Reaction of C <sub>60</sub> Uncovered with AI
We performed an extensive artificial intelligence-accelerated quasi-classical molecular dynamics investigation of the time-resolved mechanism of the Diels-Alder reaction of fullerene C<sub>60</sub> with 2,3-dimethyl-1,3-butadiene. In a substantial fraction (10%) of reactive trajectories, the larger C<sub>60</sub> noncovalently attracts the 2,3-dimethyl-1,3-butadiene long before the barrier so that the diene undergoes the series of complex motions including roaming, somersaults, twisting, and twisting somersaults around the fullerene until it aligns itself to pass over the barrier. These complicated processes could be easily missed in typically performed quantum chemical simulations with shorter and fewer trajectories. After the barrier is passed, the bonds take longer to form compared to the simplest prototypical Diels-Alder reaction of ethene with 1,3-butadiene despite high similarities in transition states and barrier widths evaluated with intrinsic reaction coordinate (IRC) calculations. C<sub>60</sub> is mainly responsible for these differences as its reaction with 1,3-butadiene is similar to the reaction with 2,3-dimethyl-1,3-butadiene: the only substantial difference being that the extra methyl groups double the probability of the prolonged alignment phase in dynamics. These additional calculations of C<sub>60</sub> with 1,3-butadiene could be performed via active learning more easily by reusing the data generated for the other two reactions, showing the potential for larger-scale exploration of the effects of different substrates in the same types of reactions.
Authors
- Pavlo O. Dral (ORCID: https://orcid.org/0000-0002-2975-9876)
- Yi-Fan Hou (ORCID: https://orcid.org/0000-0001-9188-5323)
- Quanhao Zhang (ORCID: https://orcid.org/0009-0007-6416-3561)
Institutions
- Xiamen University (CN)
- Nicolaus Copernicus University (PL)
- Institute of Physics (PL)
- Tan Kah Kee Innovation Laboratory (CN)
Publication Details
- Journal
- The Journal of Organic Chemistry
- Published
- 2024-10-03
- DOI
- https://doi.org/10.1021/acs.joc.4c01763
- Citations
- 4
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.47
Funders
- National Natural Science Foundation of China
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University
- National Outstanding Youth Foundation of China
- Fundamental Research Funds for the Central Universities