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

• 批准号: 2116647
• 负责人: Eranda Nikolla
• 金额: $ 43.25万
• 依托单位: Wayne State University
• 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 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2116647&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Machine; Learning

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.

鉴于催化反应的复杂性以及许多领域对催化剂在恶劣条件下运行的需要，催化材料长期以来一直用于提高对化学制造、石油精炼和污染控制至关重要的许多过程的效率和产品选择性。在这种情况下，大量的开发工作（尤其是工业界）已经投入到易于合成的催化剂材料的设计中，并且能够在长时间运行中保持稳定的性能，相比之下，学术研究工作主要集中在理论、计算和实验鉴定该项目创建了一个新的催化材料研究框架，将寻找更活性的材料与筛选复杂反应条件下的可合成性和稳定性结合起来。通过添加强大的机器学习（ML）方法来解决这一问题，这些方法增强了理论和计算工具，以产生与可合成、高活性和稳定的催化材料相关的更完整的属性或“描述符”。目标是以直观方法的形式打包各种发现工具，为催化从业者提供最佳结果。该项目建立在广泛实践的催化研究描述符方法的基础上，其中催化活性的描述符（例如，催化剂的吸附能）。研究工作通过开发可合成性、稳定性和活性描述符来扩展当前的方法。机器学习工具可以快速筛选这些描述符，并在迭代反馈循环中与实验人员合作，以检查预测的准确性并确保组合方法的“催化实践者友好性”。该方法将在两个重点案例研究中开发。该项目将创建一个计算机辅助工作流程和开源项目，用于在 H2 存在下进行低温优先 CO 氧化（CO PROX）和乙烯部分氧化为环氧乙烷的双金属催化剂。通过将 ML 和 DFT 建模与操作实验表征和测试相结合，将确定这两种反应的新结构-功能关系，从而预测催化剂的合成性、活性和稳定性。高度理想化系统的活性到更现实的催化系统在操作条件下的预测材料结构和成分将在反馈发现循环中根据开源高保真实验数据集进行验证，从而加速催化剂的发现。项目将包括侧重于学生专业发展、扩大科学参与和非正式科学交流的外展工作，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) 将开展跨学科培训活动。为研究生和本科生在整个职业生涯中继续取得科学进步奠定基础。 催化数据科学培训计划将使华盛顿州立大学的本科生能够在夏季访问密歇根大学，学习来自底特律学校的数据科学和催化基础知识。 ， 和他们的父母将参与由团队成员主办的科学推广活动。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。