Yuan Sihao, Han Xu, Zhang Jun, Xie Zhaoxin, Fan Cheng, Xiao Yunlong, Gao Yi Qin, Yang Yi Isaac
Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China.
J Phys Chem A. 2024 May 30;128(21):4378-4390. doi: 10.1021/acs.jpca.4c01267. Epub 2024 May 17.
Theoretical studies on chemical reaction mechanisms have been crucial in organic chemistry. Traditionally, calculating the manually constructed molecular conformations of transition states for chemical reactions using quantum chemical calculations is the most commonly used method. However, this way is heavily dependent on individual experience and chemical intuition. In our previous study, we proposed a research paradigm that used enhanced sampling in molecular dynamics simulations to study chemical reactions. This approach can directly simulate the entire process of a chemical reaction. However, the computational speed limited the use of high-precision potential energy functions for simulations. To address this issue, we presented a scheme for training high-precision force fields for molecular modeling using a previously developed graph-neural-network-based molecular model, molecular configuration transformer. This potential energy function allowed for highly accurate simulations at a low computational cost, leading to more precise calculations of the mechanism of chemical reactions. We applied this approach to study a Claisen rearrangement reaction and a carbonyl insertion reaction catalyzed by manganese.
化学反应机理的理论研究在有机化学中至关重要。传统上，使用量子化学计算来计算化学反应过渡态的人工构建分子构象是最常用的方法。然而，这种方法严重依赖个人经验和化学直觉。在我们之前的研究中，我们提出了一种研究范式，即使用分子动力学模拟中的增强采样来研究化学反应。这种方法可以直接模拟化学反应的整个过程。然而，计算速度限制了高精度势能函数在模拟中的使用。为了解决这个问题，我们提出了一种使用先前开发的基于图神经网络的分子模型——分子构型变换器来训练用于分子建模的高精度力场的方案。这种势能函数能够以较低的计算成本进行高精度模拟，从而更精确地计算化学反应机理。我们将这种方法应用于研究克莱森重排反应和锰催化的羰基插入反应。
