D3SC: Modern Data Analysis Tools for Prediction and Understanding in Catalyst Discovery

D3SC：用于预测和理解催化剂发现的现代数据分析工具

• 批准号: 1763436
• 负责人: Matthew Sigman
• 金额: $ 45.2万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763436&HistoricalAwards=false
• 关键词: D3SC; Modern; Data; Analysis; Tools

Optimizing catalytic reactions has traditionally been a highly empirical process that can be labor and material intensive and scientifically unsatisfying. A goal of chemists in the synthesis field is to streamline this catalytic reactions by developing methods to correlate reaction outputs (such as reaction rates or selectivity for a certain product) to molecular structure. The resulting mathematical relationships then can be used to predict the performance of new catalysts without spending the labor or materials to make the compounds. To truly enable this emerging data-driven approach to optimization, new methods to describe or "parameterize" molecules effectively have to be developed. To this end, in this project, funded by the Chemical Catalysis Program of the Chemistry Division, Dr. Matthew Sigman of the University of Utah and his team are defining parameters that are capable at a high level of precision of examining structural features of catalysts and starting materials in a range of catalytic reactions. The catalysts produced by this optimization method can be applied to reactions of societal importance ranging from advanced manufacturing of compounds of medical relevance to novel materials. This program is highly collaborative in that several researchers are involved in applying Dr. Sigman's techniques to modern goals in catalysis. Dr. Sigman is actively engaged in outreach activities that build upon his research. These activities include mentoring undergraduate researchers in his laboratory during the summer, building digital curriculum modules for use by undergraduates and graduate students in their studies, and performing demonstrations at local elementary schools. These diverse activities are directed at encouraging students at different stages in their education to pursue their interest in STEM fields.Dr. Sigman is building diverse structural training sets, parameter libraries, and enhanced prediction tools for ligand screening to enable rapid identification of improved ligands for catalysis. The focus of this effort is on privileged ligand scaffolds, including various diamine ligands (bipyridines, pyridine oxazolines, and bisoxazolines), monodentate and bidentate phosphines, phosphino-oxazolines, and phosphoramidites. Results for building statistically significant models are acquired both within the Sigman laboratory as well as in collaboration with a wide range of research teams. The ultimate goal is to use the resultant correlations of the reaction outputs in combination with traditional physical organic tools to evolve a greater understanding of the underlying phenomenon responsible for how a particular catalyst/substrate combination performs. Dr. Sigman is actively engaged in STEM outreach programs including mentoring undergraduate researchers in his laboratory during the summer, building digital curriculum modules for use by undergraduates and graduate students in their studies, and performing demonstrations at local elementary schools.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.

传统上，优化催化反应是一个高度经验的过程，可能是劳动和材料密集的，科学上不令人满意。合成领域的化学家的目标是通过开发将反应输出（如反应速率或特定产物的选择性）与分子结构相关联的方法来简化这种催化反应。由此产生的数学关系可以用来预测新的催化剂的性能，而无需花费人力或材料来制造化合物。为了真正实现这种新兴的数据驱动方法的优化，必须开发新的方法来有效地描述或“参数化”分子。为此，在这个由化学部门化学催化项目资助的项目中，犹他大学的马修·西格曼博士和他的团队正在确定能够在一系列催化反应中以高精度检查催化剂结构特征和起始材料的参数。通过这种优化方法产生的催化剂可以应用于具有社会重要性的反应，从医学相关化合物的先进制造到新材料。这个项目是高度合作的，几位研究人员参与了将西格曼博士的技术应用于催化的现代目标。西格曼博士积极参与以他的研究为基础的外展活动。这些活动包括夏季在他的实验室指导本科生研究人员，构建供本科生和研究生学习使用的数字课程模块，以及在当地小学进行演示。这些多样化的活动旨在鼓励处于不同教育阶段的学生追求他们对STEM领域的兴趣。Sigman正在为配体筛选构建各种结构训练集、参数库和增强的预测工具，以便快速识别用于催化的改进配体。这项工作的重点是特权配体支架，包括各种二胺配体（联吡啶，吡啶恶唑啉和双恶唑啉），单齿和双齿膦，膦-恶唑啉和磷酰胺。建立具有统计意义的模型的结果既可以在西格曼实验室获得，也可以与广泛的研究团队合作获得。最终目标是将反应输出的最终相关性与传统的物理有机工具相结合，以更好地理解特定催化剂/底物组合如何执行的潜在现象。Sigman博士积极参与STEM外展计划，包括在夏季指导他的实验室的本科生研究人员，构建供本科生和研究生学习使用的数字课程模块，并在当地小学进行演示。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Noncovalent Interactions Drive the Efficiency of Molybdenum Imido Alkylidene Catalysts for Olefin Metathesis

• DOI: 10.1021/jacs.9b04367
• 发表时间: 2019-07-10
• 期刊: JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
• 影响因子: 15
• 作者: Ferreira, Marco A. B.;Silva, Jordan De Jesus;Coperet, Christophe
• 通讯作者: Coperet, Christophe