标题： 用神经网络势能预测环化反应路径的专一性 显示英文标题

标题： Anticipating the Selectivity of Cyclization Reaction Pathways with Neural Network Potentials

摘要： 反应机理搜索工具已显示出能够提供关于反应系统可能产物和速率限制步骤的见解的能力。 然而，涉及多个协同键变化的反应——如在天然产物合成中的许多关键步骤中所见——可能会使搜索过程复杂化。 为了缓解这些复杂性，我们提出了一种特别适合帮助加速探索此类复杂反应的一个典型反应家族——环化反应的机理搜索策略。 我们提供了一种成本效益高的策略，通过结合基于图的枚举方案和机器学习技术来筛选中间体，以识别相关的基元反应步骤。 这种方法的关键是我们使用了神经网络势（NNP），即AIMNet2-rxn，用于对每个候选反应路径进行计算评估。 在本文中，我们评估了NNP估算活化能的能力，展示了正确预测立体选择性的能力，并复现了天然产物合成中的复杂促进步骤。 显示英文摘要

摘要： Reaction mechanism search tools have demonstrated the ability to provide insights into likely products and rate-limiting steps of reacting systems. However, reactions involving several concerted bond changes - as can be found in many key steps of natural product synthesis - can complicate the search process. To mitigate these complications, we present a mechanism search strategy particularly suited to help expedite exploration of an exemplary family of such complex reactions, cyclizations. We provide a cost-effective strategy for identifying relevant elementary reaction steps by combining graph-based enumeration schemes and machine learning techniques for intermediate filtering. Key to this approach is our use of a neural network potential (NNP), AIMNet2-rxn, for computational evaluation of each candidate reaction pathway. In this article, we evaluate the NNP's ability to estimate activation energies, demonstrate the correct anticipation of stereoselectivity, and recapitulate complex enabling steps in natural product synthesis.