论文标题
过渡状态的深入强化学习
Deep Reinforcement Learning of Transition States
论文作者
论文摘要
结合加固学习（RL）和分子动力学（MD）模拟，我们提出了一种机器学习方法（RL $^‡$），以自动揭示化学反应机制。在RL $^‡$中，定位化学反应的过渡状态作为游戏配制，在该游戏中，虚拟玩家经过训练以拍摄连接反应物和产品的模拟轨迹。玩家使用两个功能，一个功能用于价值估计，另一个用于策略制定，以迭代地改善赢得此游戏的机会。我们可以根据值函数直接解释反应机制。同时，该策略函数可以有效地对过渡路径进行采样，可以进一步用于分析反应动力学和动力学。通过多个实验，我们表明RL‡可以训练tabula rasa，因此我们可以揭示具有最小主观偏见的化学反应机制。
Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach (RL$^‡$) to automatically unravel chemical reaction mechanisms. In RL$^‡$, locating the transition state of a chemical reaction is formulated as a game, where a virtual player is trained to shoot simulation trajectories connecting the reactant and product. The player utilizes two functions, one for value estimation and the other for policy making, to iteratively improve the chance of winning this game. We can directly interpret the reaction mechanism according to the value function. Meanwhile, the policy function enables efficient sampling of the transition paths, which can be further used to analyze the reaction dynamics and kinetics. Through multiple experiments, we show that RL‡ can be trained tabula rasa hence allows us to reveal chemical reaction mechanisms with minimal subjective biases.
