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.

在这个项目中，由化学结构、动力学与机理B项目和化学催化项目资助，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。本研究的目标是使用密度泛函理论 (DFT) 和其他计算方法进行计算，以模拟催化反应路径并研究控制反应性和选择性的因素。这些计算研究将为过渡金属配合物和生物工程酶催化的各种碳氢键功能化反应提供机理理解。此外，还将探索研究催化剂-底物相互作用及其对反应结果影响的新计算方法，包括在回归模型中合理设计特定问题描述符以进行反应性和选择性预测的数据驱动方法。该项目的教育和推广计划旨在最大限度地发挥虚拟现实等现代计算机技术的力量，以加强有机化学概念的学习并促进合成有机化学研究。计算工具，特别是密度泛函理论（DFT）计算，已成为研究催化碳氢键功能化反应的机制、反应性和选择性的强大力量。该项目旨在探索三种类型的新计算策略，以提供关于催化剂对反应性和选择性的影响的起源的可靠且有意义的见解。刘鹏教授及其研究团队将（1）应用能量分解分析计算来分析催化剂与基质之间的非共价相互作用，并使用环应变能来分析过渡态畸变； （2）利用机制洞察来选择和设计预测回归模型的特定问题描述符； (3) 进行混合量子力学/分子力学和从头算分子动力学模拟，以研究生物工程酶催化的立体选择性反应。他们期望这些新的计算策略可以通过提供具有化学意义的实用见解来指导实验反应和催化剂开发，从而提高机械复杂催化系统的基于 DFT 的计算研究的能力。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。