  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.
