Collaborative Research: DMREF: Machine Learning-aided Discovery of Synthesizable, Active and Stable Heterogeneous Catalysts

合作研究：DMREF：机器学习辅助发现可合成、活性和稳定的多相催化剂

基本信息

批准号：
2116646
负责人：
Suljo Linic
金额：
$ 136.75万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2116646&HistoricalAwards=false
关键词：
Collaborative Research DMREF Machine Learning

项目摘要

Catalytic materials have long been used to improve the efficiency and product selectivity of many processes of vital importance to chemical manufacturing, petroleum refining, and pollution control. Given the complexity of catalytic reactions, and the need for the catalyst to operate under harsh conditions in many cases, considerable development effort – particularly from industry - has gone into the design of catalyst materials that can be readily synthesized, and that maintain stable performance for long time-on-stream. Academic research efforts, in contrast, have largely focused on theoretical, computational, and experimental identification of more active and/or lower-cost catalytic materials, but with little attention to synthesizability and stability. The project creates a new catalytic materials research framework that combines the search for more active materials with screening for synthesizability and stability under reaction conditions. The added complexity is addressed through the addition of powerful machine learning (ML) approaches that augment theoretical and computational tools to yield a more complete set of properties, or “descriptors,” associated with synthesizable, highly active, and stable catalytic materials. Ultimately, the goal is to package the various discovery tools in the form of an intuitive approach that delivers optimal results for catalysis practitioners. The project builds on the widely practiced descriptor approach to catalysis research, where a descriptor of catalytic activity (e.g., adsorption energy of an adsorbate) is computed using quantum chemical Density Functional Theory (DFT) calculations on various catalyst surfaces. Research efforts extend the current approaches by developing synthesizability, stability, and activity descriptors, using ML tools to rapidly screen through these descriptors, and collaborating with experimentalists in an iterative feedback loop to examine the accuracy of the predictions and to ensure the “catalysis practitioner-friendliness” of the combined methods. The approach will be developed in two case studies focusing on bimetallic catalysts for low temperature preferential CO oxidation in the presence of H2 (CO PROX) and partial oxidation of ethylene to ethylene oxide. The project will create a computer-aided workflow and open-source tools for predicting the synthesizability, activity, and stability of catalysts. By combining ML and DFT modeling with operando experimental characterization and testing, new structure-function relations will be identified for both reactions. In doing so, ML methods will advance beyond the prediction of activity for highly idealized systems to more realistic catalytic systems under operating conditions. Predicted materials structures and compositions will be validated against open-source high-fidelity experimental datasets in a feedback discovery loop that accelerates catalyst discovery. Beyond the technical component, the project will include outreach efforts focused on student professional development, broadened science participation, and informal science communication to help create a world-class scientific workforce. Cross-disciplinary training activities at the University of Michigan (U-M) and Wayne State University (WSU) will provide graduate and undergraduate students with a foundation to continue making scientific advances throughout their careers. A Data Science for Catalysis Training Program will enable undergraduates from WSU to visit U-M during the summer to learn the basics of data science and catalysis. Underrepresented students from Detroit schools, and their parents, will engage in science outreach events hosted by team members.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.

长期以来，催化材料已被用来提高许多对化学制造，石油炼油和污染控制至关重要的过程的效率和产品选择性。鉴于催化反应的复杂性以及在许多情况下催化剂在Harmsh条件下运行的需求，尤其是从行业中进行的大量开发工作已经进入了可以容易合成的催化剂材料的设计，并且在长时间的流程中保持稳定的性能。相比之下，学术研究工作主要集中在理论，计算和实验性鉴定上，对更活跃和/或低成本的催化材料进行了研究，但很少关注合成性和稳定性。该项目创建了一个新的催化材料研究框架，该框架将寻找更多活性材料与在反应条件下的合成性和稳定性的筛选结合在一起。通过添加强大的机器学习（ML）方法来解决增加的复杂性，从而增强理论和计算工具，以产生与合成，高度活跃且稳定的催化材料相关的更完整的属性或“描述符”集。最终，目标是以直观的方法的形式包装各种发现工具，该方法为催化实践者提供最佳的结果。该项目建立在广泛实践的催化研究中，其中使用量子化学密度函数理论（DFT）计算各种催化剂表面的催化活性描述符（例如，吸附剂的添加能量）。研究工作通过开发合成性，稳定性和活动描述符来扩展当前方法，使用ML工具快速筛选这些描述符，并与实验者合作进行迭代反馈循环，以检查预测的准确性并确保“催化练习者友好型”的合并方法。该方法将在两个案例研究中开发，重点是在H2（CO Prox）存在下低温氧化的双金属催化剂，并将部分氧化乙烯氧化为氧化乙烯。该项目将创建计算机AID的工作流程和开源工具，以预测催化剂的合成性，活性和稳定性。通过将ML和DFT建模与Operando实验表征和测试相结合，将确定两种反应的新结构 - 功能关系。这样一来，ML方法将超越高度理想化系统在运行条件下更现实的催化系统的活动的预测。预测的材料结构和组成将在反馈发现回路中的开源高保真实验数据集进行验证，以加速催化剂发现。除技术组成部分外，该项目还将包括专注于学生专业发展，扩大科学参与和非正式科学沟通的外展工作，以帮助创建世界一流的科学劳动力。密歇根大学（U-M）和韦恩州立大学（WSU）的跨学科培训活动将为研究生和本科生提供基础，以继续在整个职业生涯中取得科学进步。催化培训计划的数据科学将使WSU的本科生在夏季访问U-M，以了解数据科学和催化的基础知识。来自底特律学校及其父母的代表性不足的学生将参加由团队成员主持的科学外展活动。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估而被视为珍贵的支持。