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

长期以来，催化材料一直被用来提高许多过程的效率和产品选择性，这些过程对化学制造、石油精炼和污染控制至关重要。鉴于催化反应的复杂性，以及在许多情况下催化剂需要在苛刻的条件下运行，特别是来自工业的大量开发工作已经投入到催化剂材料的设计上，这些材料可以很容易地合成，并在长时间的生产中保持稳定的性能。相比之下，学术界的研究主要集中在理论、计算和实验上，对活性更高和/或成本更低的催化材料的鉴定，而对可合成性和稳定性的关注较少。该项目创建了一个新的催化材料研究框架，将寻找更活跃的材料与筛选在反应条件下的可合成性和稳定性结合起来。增加的复杂性是通过添加强大的机器学习(ML)方法来解决的，这些方法增强了理论和计算工具，以产生与可合成的、高活性和稳定的催化材料相关的更完整的一组性质，或称“描述符”。最终，我们的目标是将各种发现工具以直观的方式打包，为催化从业者提供最佳结果。该项目建立在催化研究中广泛使用的描述符方法的基础上，在该方法中，使用量子化学密度泛函理论(DFT)计算不同催化剂表面上的催化活性描述符(例如，吸附物的吸附能)。研究工作通过开发可综合性、稳定性和活动描述符，使用ML工具快速筛选这些描述符，并在迭代反馈循环中与实验者合作来检查预测的准确性，并确保组合方法的“催化从业者友好性”，扩展了当前的方法。该方法将在两个案例研究中开发，重点是双金属催化剂在H2(CO Prox)存在下的低温优先CO氧化和乙烯部分氧化为环氧乙烷。该项目将创建一个计算机辅助工作流程和开源工具，用于预测催化剂的可合成性、活性和稳定性。通过将ML和DFT模型与操纵腔实验表征和测试相结合，将为这两个反应确定新的结构-功能关系。这样，ML方法将超越对高度理想化体系的活性预测，而发展到更现实的操作条件下的催化体系。预测的材料、结构和成分将在加速催化剂发现的反馈发现循环中与开源高保真实验数据集进行验证。除了技术部分，该项目还将包括以学生专业发展为重点的外联工作，扩大科学参与，以及非正式的科学交流，以帮助创建一支世界级的科学队伍。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在其职业生涯中继续取得科学进步的基础。数据科学催化培训计划将使来自密歇根州立大学的本科生能够在暑假访问密歇根大学，学习数据科学和催化的基础知识。来自底特律学校的代表不足的学生及其家长将参加由团队成员主办的科学外展活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.