We propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths. By shooting multiple trajectories from these configurations, we can generate an ensemble of configurations that concentrate on the transition path ensemble. This configuration ensemble can be effectively employed in a neural network-based partial differential equation solver to obtain an approximation solution of a restricted Backward Kolmogorov equation, even when the dimension of the problem is very high. The resulting solution, known as the committor function, encodes mechanistic information for the reaction and can in turn be used to evaluate reaction rates.

翻译：我们提出了一种基于强化学习的方法，用于识别连接化学反应路径中反应物与产物状态的关键构型。通过从这些构型出发发射多条轨迹，可以生成集中于转换路径系综的构型集合。该构型集合可有效应用于基于神经网络的偏微分方程求解器，从而获得受限向后柯尔莫哥洛夫方程的近似解，即使问题维度非常高时也适用。由此得到的解——即传递函数，编码了反应的机理信息，进而可用于评估反应速率。