D3SC: Dynamic Effects in Ordinary Organic Reactions in Solution

在这个由化学系化学结构、动力学和机理B项目资助的项目中，德克萨斯农工大学的丹尼尔·A·辛格尔顿教授将研究反应机理中的“动态效应”。这些是在反应中的实验观察，既不能用标准的机理模型来预测，也不能用来解释。提出的研究有五个与动态效应相关的目标。前两个目标将探索新发现的动态效应形式，一个是由光促进的反应中可能发生的局部分子振动引起的，另一个是由现代统计速率理论中的一个缺陷引起的。第三个目标是应用新发展的对溶剂中动态效应的理解来重新解释化学的一个大的经典领域，特别是20世纪的碳正离子化学。在这个广泛的领域，有人认为，旧的争议是由于使用过于简化的机械模型而引起的。第四个目标是应用一种从动态效应中产生的技术来了解当前化学方法所隐藏的机制。其总体目标是通过将现有理论与机器学习相结合，开发一种关于动态效应的全面化学理论。总体而言，该项目将在基本和广泛的水平上影响对许多经典化学反应的理解，同时为反应的发展和控制提供新的方法。学生培训是这项研究的一个主要目标，包括将妇女和科学中代表性不足群体的成员纳入研究团队的良好记录。Singleton组的研究生和本科生承担全面的项目，这导致了不同技能的发展。它们的范围从实验有机化学和精密分析测量，到量子化学、编码、分子动力学计算和机器学习。Singleton小组还将继续开发并分发给社区的计算工具，以帮助研究反应中的动态效应。这项研究将特别研究一种间接方法，以实现长期寻求的目标，即通过振动促进复杂分子中的特定反应。要探索的想法是，不同能量的三重态光敏剂可以为分子中的局部区域提供可控制的能量。该项目还将调查变分过渡态理论中的一个缺陷，在该理论中，实际反应路径与基于势能面的预期反应路径不同，主要目标是确定受此缺陷影响的更多反应实例。第三个项目的目标是基于这样的建议，即碳阳离子化学中的历史争议是由于未被考虑的不完全溶剂化结构的中间体引起的。这一想法将在一系列著名的经典碳正离子反应中得到探索。第四个项目的目的是利用最近开发的一种动态效应--“能量读出”--来研究涉及烷氧基的反应中的关键隐藏步骤。这个想法特别适用于非绝热质子耦合电子转移(PCET)反应，这些反应的机理无法通过常规的实验和计算方法获得。最终目标是通过使用机器学习将过渡状态划分为动态结果区域来修改过渡状态理论。这包括开发主动学习和提高精度的方法，旨在简化轨迹对动态效果的应用，以及向更广泛的化学界提供这些计算工具。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In this project funded by the Chemical Structure, Dynamics, and Mechanism-B Program of the Chemistry Division, Professor Daniel A. Singleton of Texas A&M University will investigate “dynamic effects” in reaction mechanisms. These are experimental observations in reactions that can neither be predicted nor explained using the standard models for mechanisms. The proposed research has five aims related to dynamic effects. The first two aims would explore newly identified forms of dynamic effects, one resulting from localized molecular vibrations that can occur in reactions promoted by light and another that arises from a flaw in modern statistical rate theories. A third aim is to apply newly developed understanding of dynamic effects in solvent to reinterpret a large classical area of chemistry, specifically 20th century carbocation chemistry. In this broad field, it is suggested that old controversies arose from the use of mechanistic models that were too simplified. A fourth aim is to apply a technology that arises from a dynamic effect to learn about mechanisms that are hidden from current chemical approaches. The overarching aim is to develop a comprehensive chemical theory of dynamic effects by combining current theories with machine-learning. Overall, this project will impact at a fundamental and broad level the understanding of many classic chemical reactions, while providing new approaches to the development and control of reactions. Student training is a major goal of this research including with a good record of including women and members of underrepresented groups in science as part of the research team. Graduate and undergraduate students in the Singleton group undertake comprehensive projects, which lead to the development of diverse skills. These range from experimental organic chemistry and precision analytical measurements, to quantum chemistry, coding, molecular dynamics calculations, and machine learning. The Singleton group will also continue its program of developing and distributing to the community computational tools that aid the study of dynamic effects in reactions. The research will particularly investigate an indirect approach to the long-sought goal of vibrationally promoting specific reactions in complex molecules. The idea to be explored is that triplet photosensitizers of varying energies can provide a controllable amount of energy to localized regions in molecules. The project will also investigate a flaw in variational transition state theory in which the actual reaction path departs from that expected based on the potential energy surface, with the primary goal of identifying additional examples of reactions subject to this flaw. The third project aim is based on the proposal that historical controversies in carbocation chemistry have resulted from the unconsidered intermediacy of incompletely solvated structures. This idea will be explored within a series of prominent classical carbocation reactions. The fourth project aim is to use a recently developed application of a dynamic effect, “energy read-out,” to study key hidden steps in reactions involving alkoxy radicals. This idea is notably applicable to non-adiabatic proton coupled electron transfer (PCET) reactions where mechanisms are inaccessible through ordinary experimental and computational methods. The final aim is to modify transition state theory by partitioning the transition state into regions of dynamical outcome using machine learning. This includes the development of active-learning and precision-enhancing methods designed to ease the application of trajectories to dynamic effects, as well as the provision of these computational tools to the wider chemical community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.