Title: Concentration-Flux-Steered Mechanism Exploration with an Organocatalysis Application Show Chinese title

Title: 基于有机催化应用的浓度-通量导向机制探索

Abstract: Investigating a reactive chemical system with automated reaction network exploration algorithms provides a more detailed picture of its chemical mechanism than what would be accessible by manual investigation. In general, exploration algorithms cannot uncover reaction networks exhaustively for feasibility reasons. They should therefore decide which part of a network is kinetically relevant under some external conditions given. Here, we propose an automated algorithm that identifies and explores kinetically accessible regions of a reaction network on the fly by explicit modeling of concentration fluxes through an (incomplete) reaction network that is emerging during automated first-principles exploration. Key compounds are automatically identified and selected for the continuation of the exploration. As an example, we explore the reaction network of the multi-component proline-catalyzed Michael addition of propanal and nitropropene. Our algorithm provides a mechanistic picture of the Michael addition in unprecedented detail. Show Chinese abstract

Abstract: 通过使用自动化反应网络探索算法来研究反应性化学系统，可以比手动研究更详细地揭示其化学机理。 一般来说，探索算法由于可行性原因无法彻底揭示反应网络。 因此，它们应根据给定的外部条件决定网络中哪些部分在动力学上是相关的。 在这里，我们提出了一种自动化算法，该算法通过显式建模浓度通量，实时识别并探索反应网络中动力学可访问区域，该反应网络是在自动化第一性原理探索过程中逐渐形成的。 关键化合物会被自动识别并选择以继续探索。 作为示例，我们探索了多组分脯氨酸催化丙醛和硝基丙烯的迈克尔加成反应网络。 我们的算法提供了对迈克尔加成反应前所未有的详细机理图景。