CAREER: Microkinetic Modeling-Driven Discovery of Molecular Catalysts

职业：微动力学模型驱动的分子催化剂发现

• 批准号: 2339481
• 负责人: Tibor Szilvasi
• 金额: $ 64.33万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-08-15 至 2029-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339481&HistoricalAwards=false
• 关键词: CAREER; Microkinetic; Modeling; Driven; Discovery

With support from the Chemical Catalysis Program in the Division of Chemistry, Tibor Szilvási of the University of Alabama-Tuscaloosa is focusing on computational tools that empower the design of efficient molecular catalysts. Dr. Szilvási will develop computational methods to investigate catalytic reactions relevant to the chemical industry. His research group will be using high-performance computational screening to identify optimal catalyst structures, compositions, and kinetics to increase overall activity, selectivity, and stability. Dr. Szilvási will also design a new course that is designed to provide advanced programming expertise to undergraduate students in chemical engineering. This training will help prepare students for computationally focused jobs in the chemical sciences. Additional lesson plans will be developed for teaching programming to high school students and to help encourage underrepresented minority students to pursue advanced degrees. This proposed work will involve electronic structure calculations and microkinetic modeling to accelerate the design of molecular catalysts, with an emphasis on obtaining quantitative agreement between experiments and simulations. The PI will develop and use microkinetic modeling to analyze reaction networks and transform computational data into a standard format often used in kinetic measurements. This will enable direct benchmarking and verification between kinetic measurements and simulations in an easily accessible manner, which will be understandable to experimentalists who are not experts in computations. This proposal will address the mentioned challenges and focus on three systems: (i) nitrogen fixation using biomimetic organometallic complexes; (ii) hydrogen activation using main group catalysts; and (iii) light-driven carbon dioxide (CO2) reduction.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.

在化学系化学催化项目的支持下，塔斯卡卢萨大学的Tibor Szilvási专注于设计高效分子催化剂的计算工具。Szilvási博士将开发计算方法来研究与化学工业相关的催化反应。他的研究小组将使用高性能计算筛选来确定最佳的催化剂结构，组成和动力学，以提高整体活性，选择性和稳定性。 Szilvási博士还将设计一门新课程，旨在为化学工程专业的本科生提供高级编程专业知识。 这项培训将帮助学生为化学科学中以计算为重点的工作做好准备。将制定更多的课程计划，向高中学生教授编程，并帮助鼓励代表性不足的少数民族学生攻读高等学位。这项拟议的工作将涉及电子结构计算和微观动力学建模，以加速分子催化剂的设计，重点是获得实验和模拟之间的定量协议。PI将开发和使用微动力学建模来分析反应网络，并将计算数据转换为动力学测量中常用的标准格式。这将以易于访问的方式实现动力学测量和模拟之间的直接基准测试和验证，这对于不是计算专家的实验人员来说是可以理解的。该提案将解决上述挑战，并重点关注三个系统：（i）使用仿生有机金属络合物的固氮;（ii）使用主族催化剂的氢活化;（iii）光驱动的二氧化碳（CO2）还原。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。