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

合作研究：DMREF：用于选择性键激活的原子精确催化剂设计

• 批准号: 2323701
• 负责人: John Vohs
• 金额: $ 53.82万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323701&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Atomically; precise

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.

该项目开发了一种负载型单原子催化剂（SAC）的设计方法，这是一类新兴的负载型单金属原子催化剂，它提供了令人兴奋的新兴特性，可以彻底改变许多工业应用，但由于了解有限，阻碍了其全部潜力的实现。如何在复杂的材料设计空间中控制它们的稳定性和催化性能，延伸到金属原子和支撑材料的性能，以及两者之间的相互作用。为了克服这一挑战，该项目采用了高度集成的、使用机器学习技术（ML）的计算实验方法，利用支撑材料作为配体来调节金属位点的几何和电子特性并提高其稳定性。模型预测将指导合成、表征和催化测量，以实现选择性。所提出的方法可以深刻影响用于具有挑战性的化学反应的复杂材料的发现，同时最大限度地提高单原子水平的金属利用率，可以显着降低资本成本和能源消耗。 ，从而降低二氧化碳排放量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获得多相催化方面的独特教育经验。此外，该项目还将开发针对 K-12 学生的外展项目的教育材料，重点是增加 STEM 领域代表性不足的学生的参与。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活性、稳定和选择性金属载体成分的指导原则。该模型预测将指导单金属的合成。通过原子层沉积将原子负载在新型高表面积非常规支撑材料（钙钛矿和尖晶石）上，然后对其性能进行详细表征、催化剂评估以及模型评估和细化（从而实现高效的催化剂发现/设计通过揭示物理启发的描述符并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点的局部阳离子环境如何影响稳定性、活性和选择性。模型将针对两个复杂的工业相关反应进行评估：1）水煤气变换和2）甲酚加氢脱氧（HDO）生成甲苯。前者主要侧重于最大化反应速率，而后者则涉及活性和甲苯。这项研究的成果将作为计算机设计新材料的基础方法。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。