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 uses 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 limits the use of high-precision potential energy functions for simulations. To address this issue, we present 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 allows 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.

翻译：关于化学反应机理的理论研究在有机化学中至关重要。传统上，通过量子化学计算手动构建化学反应过渡态的分子构型是最常用的方法。然而，这种方式严重依赖于个人经验与化学直觉。在我们先前的研究中，提出了一种利用分子动力学模拟中增强采样技术研究化学反应的研究范式，该方法能够直接模拟化学反应的全过程。然而，计算速度限制了高精度势能函数在模拟中的应用。为解决这一问题，我们提出了一种方案：利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，训练分子建模所需的高精度力场。该势能函数能以较低计算成本实现高精度模拟，从而更精确地计算化学反应机理。我们将此方法应用于克莱森重排反应和锰催化的羰基插入反应的研究。