Multidimensional Parameterization in the Analysis of Selective Catalytic Reactions

在这个由化学部化学催化计划资助的项目中，犹他州大学化学系的Matthew Sigman教授探索了提高预测化学反应结果能力的技术发展。 这一奋进的成功对任何需要分子合成和反应优化的追求都有影响，例如制药，化学，农业工业及其生物和化学研究活动。 除了建立一个国际公认的计划，该项目提供了优秀的学生培训，从本科前到博士后，包括那些来自历史上在科学中代表性不足的群体。 预测化学反应结果的能力需要在有限数量的精心设计的初始实验中构建与物理有机化学规则（描述空间和电子效应的参数）相关的数学模型。 所得到的模型被用来预测一个给定的催化反应的催化剂和基板性能的新的例子。 要做到这一点，新的物理有机参数和设计概念的发展。这项工作导致新的方法来询问反应外推的模型来预测新的催化剂和底物的性能为给定的反应。 此外，由此产生的模型增强了我们对反应机制的理解，因为参数与物理有机概念有关。

In this project funded by the Chemical Catalysis Program of the Chemistry Division, Professor Matthew Sigman of the Department of Chemistry at the University of Utah explores the development of techniques to enhance the ability to predict the outcomes of chemical reactions. Success in this endeavor has an impact on any pursuit in which the synthesis of molecules and reaction optimization is needed, such as the pharmaceutical, chemical, agricultural industries and their biological and chemical research activities. In addition to building an internationally recognized program, this project provides excellent training of students, from pre-undergraduate to post-doctoral, including those from groups historically underrepresented in the sciences. The ability to predict the outcome of a chemical reaction necessitates the construction of mathematical models relating to physical organic chemical precepts (parameters describing steric and electronic effects) in a limited number of well-designed initial experiments. The resultant models are used to predict new examples of both catalyst and substrate performance for a given catalytic reaction. To accomplish this, new physical organic parameters and design concepts are to be developed. This work leads to novel ways to interrogate reactions in terms of extrapolating the models to predict the performance of new catalysts and substrates for a given reaction. Additionally, the resulting models enhance our understanding of reaction mechanisms, since the parameters are tied to physical organic concepts.