AI 中文总结
研究针对过渡态搜索计算量大且依赖初始猜测等问题，基于软演员-评论家模型，将搜索设为内部坐标的序列决策过程，通过奖励函数自适应更新结构，成功识别标准反应过渡态几何结构，展现强化学习在反应发现中的潜力。
AI 中文摘要
过渡态搜索是理解化学反应性和机理的关键步骤，但传统算法计算量大，且严重依赖初始猜测、用户专业知识和化学直觉。近期机器学习方法虽有前景，但存在局限性。本文引入基于软演员-评论家模型的过渡态搜索模型，该模型让智能体从给定反应物及其产物出发，根据局部能量和曲率信息在势能面上导航。通过将搜索构建为内部坐标中的序列决策过程，智能体借助奖励函数自适应地提出有化学意义的结构更新，成功识别了标准基准反应的过渡态几何结构，凸显了强化学习在减少对初始猜测的依赖及实现跨化学系统的可扩展、自动化反应发现方面的潜力。
英文摘要
Transition state (TS) search is a crucial step in understanding chemical reactivity and mechanisms, yet conventional algorithms remain computationally intensive and heavily reliant on initial guesses, user s expertise, and chemical intuition. While recent machine learning approaches have shown promise, they demand either large training datasets or geometric interpolation between known endpoints, limiting their generality. In this work, we introduce a TS search model based on the soft actor-critic model, an advanced reinforcement learning algorithm in which an agent learns to navigate potential energy surfaces directly from local energetic and curvature information starting from a given reactant and its corresponding product. By formulating the search as a sequential decision-making process in internal coordinates, the agent adaptively proposes chemically meaningful structural updates through a reward function designed to promote movement towards saddle point regions. Without labelled trajectories or prescribed reaction pathways, the method successfully identifies TS geometries for standard benchmark reactions, operating directly on realistic molecular potential energy surfaces. These results highlight the potential of RL as a general strategy for reducing dependence on initial guesses and enabling scalable, automated reaction discovery across diverse chemical systems.
