Computational Studies of Selective C-H Functionalization Reactions

选择性 C-H 官能化反应的计算研究

• 批准号: 2247505
• 负责人: Peng Liu
• 金额: $ 45万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2247505&HistoricalAwards=false
• 关键词: Computational; Studies; Selective; Functionalization; Reactions

In this project, funded by the Chemical Structure, Dynamics & Mechanisms B Program and the Chemical Catalysis program, Professor Peng Liu of the Department of Chemistry at the University of Pittsburgh is applying computational tools to study catalytic carbon hydrogen bond functionalization reactions. The goal of this research is to perform computations using density functional theory (DFT) and other computational methods to model catalytic reaction pathways and investigate factors that control reactivity and selectivity. These computational studies will provide mechanistic understanding of various carbon hydrogen bond functionalization reactions catalyzed by transition metal complexes and bioengineered enzymes. In addition, new computational approaches to investigate catalyst–substrate interactions and their impacts on reaction outcomes, including data-driven approaches to rationally design problem-specific descriptors in regression models for reactivity and selectivity predictions, will be explored. This project's educational and outreach plan aims to maximize the power of modern computer technology, such as virtual reality, to enhance learning of organic chemistry concepts and to facilitate synthetic organic chemistry research.Computational tools, in particular, density functional theory (DFT) calculations, have emerged as a potent force to investigate the mechanisms, reactivity, and selectivity of catalytic carbon hydrogen bond functionalization reactions. This project aims to explore three types of new computational strategies to provide robust and meaningful insights into the origin of catalyst effects on reactivity and selectivity. Professor Peng Liu and his research team will (1) apply energy decomposition analysis calculations to analyze non-covalent interactions between catalyst and substrate and use ring strain energies to analyze transition state distortion; (2) use mechanistic insights to select and design problem-specific descriptors for predictive regression models; and (3) perform hybrid quantum mechanics/molecular mechanics and ab initio molecular dynamics simulations to study stereoselective reactions catalyzed by bioengineered enzymes. They expect that these new computational strategies can increase the capability of DFT-based computational studies of mechanically complex catalytic systems by offering chemically meaningful, practical insights to guide experimental reaction and catalyst developments.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.

在该项目中，由化学结构，动力学和机制B计划和化学催化计划资助，匹兹堡大学化学系的Peng Liu教授正在应用计算工具来研究催化碳氢键功能化反应。这项研究的目的是使用密度功能理论（DFT）和其他计算方法进行计算，以模拟催化反应途径并研究控制反应性和选择性的因素。这些计算研究将提供对通过过渡金属复合物和生物工程酶催化的各种碳氢键功能化反应的机械理解。此外，将探索新的计算方法，以研究催化剂 - 基底相互作用及其对反应结果的影响，包括在回归模型中以数据为驱动的方法进行反应性和选择性预测的方法。该项目的教育和外展计划旨在最大程度地发挥现代计算机技术的力量，例如虚拟现实，以增强有机化学概念的学习，并促进合成有机化学研究。Computitation.compuputitation工具，尤其是密度功能理论（DFT）计算，已成为研究机制的反应性，反应性碳水化合物功能的潜在力量。该项目旨在探索三种新型的计算策略，以提供对催化剂对反应性和选择性影响的起源的强大而有意义的见解。 Peng Liu教授和他的研究团队（1）应用能量分解分析计算来分析催化剂与底物之间的非共价相互作用，并使用环应变能来分析过渡状态变形； （2）使用机械洞察来选择和设计特定问题的描述符，以进行预测回归模型； （3）执行杂交量子力学/分子机械和从头算分子动力学模拟来研究由生物工程酶催化的立体选择反应。他们预计，这些新的计算策略可以通过提供化学意义上有意义的实用见解来指导实验反应和催化剂的发展，从而提高DFT基于DFT的计算研究的能力。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和广泛的影响来评估NSF的法定任务。