CAREER: CDS&E: Predictive Discovery of Complex Reaction Mechanisms

职业：CDS

• 批准号: 1551994
• 负责人: Paul Zimmerman
• 金额: $ 62.5万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551994&HistoricalAwards=false
• 关键词: CAREER; CDS& Predictive; Discovery; Complex

Paul Zimmerman at the University of Michigan is supported by the Chemical Theory, Models and Computational Methods Program and the Computational and Data-Enabled Science and Engineering (CDS&E) Program to develop new tools to predict the outcome of chemical reactions. Computational chemistry has a long history of using the principles of quantum mechanics to create tools which provide detailed, accurate explanations for a wide variety of chemical processes. Many of these tools have reached sufficient accuracy that they can be applied to discover chemical reactions without requiring prior insight from experiment. Unfortunately, the high computational cost of these methods has prevented their broad use in predicting chemical reactivity, especially in cases where the chemistry is highly complex or poorly understood. This grant supports the development of fully computational, low-cost and highly accurate methods that are able to predict the outcome of chemical reactions starting only from the feedstock molecules. The application focus of these methods is catalytic reactions, providing a highly useful tool for research into chemistries that can be applied at an industrial level to create high-value chemical products. These tools are available to the wider computational chemistry community, enabling maximized impact of the developments to a great number of problems in chemical reactivity. Due to the diversity of potential applications for this research, the students involved in this project gain not only algorithm development abilities, but also fundamental insights into processes governing molecular behavior, cutting-edge computational research experience, and problem-solving abilities that are needed to address the challenges of the 21st century. The proposed methods are transformed into educational strategies that merge introductory laboratory exercises with real-world research, starting with a pilot study in honors organic chemistry. The research program builds upon work by the Zimmerman group that shows chemical reactions can be described in terms of a small number of localized, anharmonic reaction coordinates. These reaction coordinates consist of interatomic distances, angles, and torsions, and are a reliable, transferable basis for describing atomic motion. By employing advanced single-ended chain-of-states optimization algorithms which search along these coordinates for plausible chemical reactions, this research methodology can efficiently and reliably predict reactive events without guidance from chemical intuition. The main objective of ongoing work is to expand this method to cover transition metal elements, enable efficient conformation searches in systems with floppy degrees of freedom, and develop machine learning algorithms to automatically process chemical data and provide great enhancements in computational efficiency. In sum, these new reaction search techniques enable predictive reaction discovery in a wide variety of large, highly complicated chemical systems where chemical knowledge alone is not yet sufficient to make accurate predictions. These methods are being applied to challenging cases in catalysis where uncharacterized side reactions are severe impediments to efficient product formation. Ongoing discovery of these undesired reaction pathways lead to the chemical insight required to improve reaction selectivity and catalyst stability.These tools allow beginning chemistry students to hypothesize and evaluate reactions in silico, resulting in a means for students to perform research at an early stage of their studies. Such pedagogical tools are distributed to educators outside of the University of Michigan to maximize their impact.

密歇根大学的Paul齐默尔曼得到了化学理论，模型和计算方法计划以及计算和数据支持的科学与工程（CDS E）计划的支持，以开发新的工具来预测化学反应的结果。计算化学在使用量子力学原理创建工具方面有着悠久的历史，这些工具为各种化学过程提供了详细，准确的解释。这些工具中的许多已经达到了足够的精度，可以应用于发现化学反应，而不需要事先从实验中获得洞察力。不幸的是，这些方法的高计算成本已经阻止了它们在预测化学反应性中的广泛使用，特别是在化学高度复杂或理解不足的情况下。该补助金支持开发完全计算，低成本和高度准确的方法，这些方法能够预测仅从原料分子开始的化学反应的结果。这些方法的应用重点是催化反应，为研究化学提供了非常有用的工具，可以应用于工业水平，以创造高价值的化学产品。这些工具可供更广泛的计算化学界使用，使发展对化学反应性中大量问题的影响最大化。由于这项研究的潜在应用的多样性，参与该项目的学生不仅获得了算法开发能力，而且还获得了对分子行为过程的基本见解，尖端的计算研究经验以及解决21世纪世纪挑战所需的问题解决能力。所提出的方法转化为教育策略，将介绍性实验室练习与现实世界的研究相结合，从荣誉有机化学的试点研究开始。该研究计划建立在齐默尔曼小组的工作基础上，该小组表明化学反应可以用少量的局部非谐反应坐标来描述。这些反应坐标由原子间的距离、角度和挠率组成，是描述原子运动的可靠、可转移的基础。通过采用先进的单端链的状态优化算法，搜索沿着这些坐标的合理的化学反应，这种研究方法可以有效地和可靠地预测反应事件，而无需化学直觉的指导。正在进行的工作的主要目标是将这种方法扩展到过渡金属元素，在具有松弛自由度的系统中实现有效的构象搜索，并开发机器学习算法以自动处理化学数据并大大提高计算效率。总之，这些新的反应搜索技术使得能够在各种各样的大型、高度复杂的化学系统中发现预测反应，在这些系统中，仅凭化学知识还不足以做出准确的预测。这些方法正被应用于催化中具有挑战性的情况，其中未表征的副反应是有效产物形成的严重障碍。这些不期望的反应途径的持续发现带来了提高反应选择性和催化剂稳定性所需的化学洞察力。这些工具允许化学初学者通过计算机模拟假设和评估反应，为学生在早期阶段进行研究提供了一种手段他们的学习。这些教学工具分发给密歇根大学以外的教育工作者，以最大限度地发挥其影响。