Collaborative Research: DMREF: Atomically precise catalyst design for selective bond activation

该项目开发了负载单原子催化剂(SACS)的设计方法-这是一种新兴的负载单金属原子催化剂，具有令人兴奋的和新出现的特性，可以彻底改变许多工业应用。由于对如何在金属原子和支撑材料的性质以及两者之间的相互作用的复杂材料设计空间内控制其稳定性和催化性能的有限理解，阻碍了它们全部潜力的实现。为了克服这一挑战，该项目采用了一种高度集成的计算-实验方法，使用机器学习技术(ML)来利用支撑材料作为配体来调节金属位置的几何和电子性质并提高其稳定性。模型预测将指导合成、表征和催化测量，以实现选择性键激活。所提出的方法可以深刻地影响复杂材料的发现，以挑战化学反应。设计稳定、活性和选择性的催化剂，同时最大限度地提高单原子水平的金属利用率，可以显著降低资本成本和能源消耗，从而降低二氧化碳排放，减少有害副产品的产生，并更负责任地利用碳氢化合物原料。这项研究的跨学科性质以及三个机构之间研究和教育计划的整合将导致一批学生在多相催化、多尺度建模以及基于实验室和同步加速器的高级表征技术方面获得独特的教育经验。此外，该项目将为针对K-12学生的外展项目开发教育材料，重点努力增加未被充分代表的学生在STEM领域的参与。该项目结合了以人工智能(AI)和基于多尺度建模的方法为中心的概念框架，以建立可用于预测高度活跃、稳定和选择性的金属支架成分的指导原则。模型预测将指导通过原子层沉积的新的、高比表面积的非传统载体材料(钙钛矿和尖晶石)上负载的单金属原子的合成，随后将详细描述其性质、催化剂评估以及模型评估和改进(从而实现高效的催化剂发现/设计循环)。通过揭示受物理启发的描述符和利用机器学习的能力，该项目旨在预测氧化物载体的表面组成和金属位置的局部阳离子环境如何影响稳定性、活性和选择性。开发的方法和模型将根据两个复杂的工业相关反应进行评估：1)水-气变换，2)甲酚加氢脱氧(HDO)为甲苯。前者主要侧重于最大限度地提高反应速度，而后者则同时解决活性和选择性方面的挑战。这项研究的结果将作为设计硅新材料的基本方法。这一奖项反映了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。

The project develops a design methodology for supported single-atom catalysts (SACs) – an emerging class of supported single metal-atom catalysts that offer exciting and emergent properties that can revolutionize many industrial applications. The realization of their full potential is hindered by limited understanding of how to control their stability and catalytic properties within the complex material design space extending across the properties of the metal atoms and supporting material, together with interactions between the two. To overcome this challenge, the project embraces a highly-integrated, computational-experimental methodology using machine learning techniques (ML) to leverage the support material as a ligand to regulate the geometric and electronic properties of the metal site and improve its stability. The model predictions will guide the synthesis, characterization and catalytic measurements to enable selective bond activation. The proposed methodology can profoundly impact the discovery of complex materials for challenging chemical reactions. The design of stable, active, and selective catalysts, while maximizing the metal utilization at the single-atom level, can significantly reduce capital costs and energy consumption, leading to lower CO2 emissions, reduced production of harmful byproducts, and more responsible utilization of hydrocarbon feedstocks. The interdisciplinary nature of this research and the integration of research and education plans between the three institutions will lead to a cadre of students obtaining a unique educational experience in heterogeneous catalysis, multiscale modeling, and advanced lab- and synchrotron-based characterization techniques. Furthermore, the project will develop educational materials for outreach programs targeting K-12 students with focused efforts to increase the participation of underrepresented students in STEM fields.The project incorporates a conceptual framework centered on artificial intelligence (AI) and multiscale modeling-based methodologies to build guiding principles that can be leveraged to predict highly active, stable, and selective metal-support compositions. The model predictions will guide the synthesis of single-metal atoms supported on novel, high-surface-area unconventional support materials (perovskites and spinels) by atomic layer deposition, followed by detailed characterization of their properties, catalyst evaluation, and model assessment and refinement (thus enabling an efficient catalyst discovery/design loop). By uncovering physics-inspired descriptors and harnessing the capabilities of machine learning, the project aims to predict how the surface composition of the oxide support and the local cation environment at the metal site influence stability, activity, and selectivity. The developed methods and models will be evaluated with respect to two complex industrially relevant reactions: 1) water-gas shift, and 2) hydrodeoxygenation (HDO) of cresol to toluene. The former focuses primarily on maximizing reaction rate, while the latter addresses both activity and selectivity challenges. The outcome of this research will serve as a foundational methodology for designing new materials in silico.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.