Computational Models for New Developments in Radical Chemistry

项目摘要 该项目的目标是开发新的理解和预测模型的形成，反应性， 有机自由基和双自由基中间体的选择性。三重态双自由基可以经历各种 在单重态基态中不可访问的转换。然而，开发高效的光催化剂 三重态能量转移方法，特别是以对映选择性方式的三重态能量转移方法，仍然是一个持久的目标。我们将 显示计算方法可以被用来开发基质和敏化剂的一般原理 利用三重态反应的设计我们将使用计算来定位机制引导的发现， 三重态的未探索的反应性，如均裂芳族取代，以及手性刘易斯的设计 酸以促进不对称光催化环化。我们还将制定一个定性和定量的 了解控制自由基均裂取代反应速率和位点选择性的因素， 例如氢和卤素原子转移反应。这些概念性的见解将被用来合理化 实验观察和支持新的自由基试剂的发展，为位点选择性的C（sp3）-H 氯化。在新的物理-有机参数的帮助下， 来加速这个过程。基于机械理解的机器学习模型将提供新的 参数化底物和试剂的工具，以加速反应发现和优化。我们会委聘 该策略用于预测P450氧化小分子的位点选择性并建立一般工作流程 来预测代谢降解途径。

Project Abstract The goal of this project is to develop new understanding and predictive models for the formation, reactivity, and selectivity of organic radical and diradical intermediates. Triplet diradicals can undergo a variety of transformations that are not accessible in the singlet ground state. However, developing efficient photocatalytic triplet energy transfer processes, particularly in an enantioselective fashion, remains an enduring goal. We will show that computational approaches can be leveraged to develop general principles for substrate and sensitizer design to harness triplet-state reactivity. We will use computation to target the mechanism-guided discovery of unexplored reactivity in the triplet state, such as homolytic aromatic substitution, and the design of chiral Lewis acids to promote asymmetric photocatalytic cyclizations. We will also develop a qualitative and quantitative understanding of the factors controlling the reaction rates and site selectivities of radical homolytic substitution, such as hydrogen and halogen atom transfer reactions. These conceptual insights will be used to rationalize experimental observations and underpin the development of new radical reagents for site-selective C(sp3)-H chlorination. The development of quantitative models, aided by new physical-organic parameters, can be used to accelerate this process. Machine learning models grounded in mechanistic understanding will provide new tools to parametrize substrates and reagents to accelerate reaction discovery and optimization. We will employ this strategy to predict the site-selectivity of P450 oxidation small molecules and to establish general workflows to predict the metabolic degradation pathways.