Hou Yi-Fan, Zhang Quanhao, Dral Pavlo O
State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.
Institute of Physics, Faculty of Physics, Astronomy, and Informatics, Nicolaus Copernicus University in Toruń, Ul. Grudziądzka 5, Toruń 87-100, Poland.
J Org Chem. 2024 Oct 18;89(20):15041-15047. doi: 10.1021/acs.joc.4c01763. Epub 2024 Oct 2.
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 with 2,3-dimethyl-1,3-butadiene. In a substantial fraction (10%) of reactive trajectories, the larger C 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 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 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.
我们进行了一项广泛的人工智能加速准经典分子动力学研究，以探究富勒烯C与2,3 - 二甲基 - 1,3 - 丁二烯的狄尔斯 - 阿尔德反应的时间分辨机制。在相当一部分（10%）的反应轨迹中，较大的C在越过势垒之前很久就非共价地吸引2,3 - 二甲基 - 1,3 - 丁二烯，使得二烯围绕富勒烯经历一系列复杂运动，包括漫游、翻跟斗、扭转以及扭转翻跟斗，直到它调整自身以越过势垒。在通常进行的较短轨迹且数量较少的量子化学模拟中，这些复杂过程很容易被忽略。越过势垒后，与乙烯和1,3 - 丁二烯最简单的典型狄尔斯 - 阿尔德反应相比，尽管通过内禀反应坐标（IRC）计算评估的过渡态和势垒宽度高度相似，但键形成所需的时间更长。C主要导致了这些差异，因为它与1,3 - 丁二烯的反应与与2,3 - 二甲基 - 1,3 - 丁二烯的反应相似：唯一显著的差异是额外的甲基使动力学中延长对齐阶段的概率增加了一倍。通过重用为其他两个反应生成的数据，可以更轻松地通过主动学习对C与1,3 - 丁二烯进行这些额外计算，这显示了在同一类型反应中对不同底物效应进行更大规模探索的潜力。
