Transition states (TSs) are transient structures that are key in
understanding reaction mechanisms and designing catalysts but challenging to be
captured in experiments. Alternatively, many optimization algorithms have been
developed to search for TSs computationally. Yet the cost of these algorithms
driven by quantum chemistry methods (usually density functional theory) is
still high, posing challenges for their applications in building large reaction
networks for reaction exploration. Here we developed React-OT, an optimal
transport approach for generating unique TS structures from reactants and
products. React-OT generates highly accurate TS structures with a median
structural root mean square deviation (RMSD) of 0.053{\AA} and median barrier
height error of 1.06 kcal/mol requiring only 0.4 second per reaction. The RMSD
and barrier height error is further improved by roughly 25% through pretraining
React-OT on a large reaction dataset obtained with a lower level of theory,
GFN2-xTB. We envision the great accuracy and fast inference of React-OT useful
in targeting TSs when exploring chemical reactions with unknown mechanisms.

React-OT 是一种快速生成过渡态结构的优化传输方法，在化学反应的探索中具有高准确性和快速推断的潜力。

React-OT: 化学反应中生成过渡态的最优输运

React-OT: Optimal Transport for Generating Transition State in Chemical  Reactions

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 QM/MM 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 our
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 have used this approach to study a
Cope rearrangement reaction and a Carbonyl insertion reaction catalyzed by
Manganese. This "AI+Physics" based simulation approach is expected to become a
new trend in the theoretical study of organic chemical reaction mechanisms.

我们提出了基于增强采样的 QM/MM 分子动力学模拟的研究范式，并通过我们开发的基于图神经网络的分子模型 —— 分子构型变换器，进行了高精度力场的训练。这种方法可以以较低的计算成本进行高精度模拟，从而更准确地计算化学反应机制，预计将成为有机化学反应机制理论研究的新趋势。

使用分子配置转换器生成用于研究化学反应机理的高精度分子动力学模拟力场

Generating High-Precision Force Fields for Molecular Dynamics  Simulations to Study Chemical Reaction Mechanisms using Molecular  Configuration Transformer