Collaborative Research: DMREF: Accelerated Discovery and Design of Dynamically Evolving Catalyst Material Surfaces

About This Grant

Catalyst materials that speed up chemical reactions play a critical role in the production of energy and chemicals. The catalyst can change during this process, as metal atoms rearrange on the nanoscale, forming new structures with distinct properties and performance. Manipulating such changes could lead to improved materials for industrial reactions, but research progress has been limited by a lack of general principles to understand and control catalyst dynamics. To address this challenge, researchers will integrate advanced computer modeling, accelerated by artificial intelligence and machine learning, with experimental tools to study how catalyst structures evolve during reactions. This workflow enables efficient screening of a wide range of materials to accelerate the discovery and design of more effective catalysts by controlling their dynamics. Specifically, the project will study ammonia fertilizer production, which supports global food supply but is highly energy-intensive (~2% of annual global energy consumption goes to this process), to guide the design of new energy-efficient catalysts. The project will also study how ammonia can be used as an energy carrier through cracking to hydrogen over earth-abundant catalysts. Interdisciplinary training of graduate students in state-of-the-art computer modeling and experimental methods, combined with educational outreach efforts to K-12 students, will prepare students to become leaders in catalytic materials design. This project will construct a unified, predictive model of the dynamic restructuring of metal nanoparticles on metal-oxide supports by elucidating the effects of materials properties and reaction environments on dynamic catalyst performance. In turn, these principles will enable the design of more active, stable, and ‘self-healing’ materials for industrially relevant ammonia synthesis and cracking reactions by tuning material properties to stabilize the most active nanostructures under reaction conditions, and enabling regeneration treatments that reverse the deleterious effects of catalyst sintering. The research team will develop a closed-loop workflow to integrate ab initio molecular modeling and artificial intelligence/machine learning (AI-ML) tools to efficiently screen materials composition space, combined with experimental synthesis of shape-controlled metal nanoparticles on metal-oxide supports, in situ characterization of dynamic behavior using high-resolution microscopy and spectroscopy, and high-throughput reactivity evaluation using steady-state and transient methods. Insights from this project will be used to develop more energy-efficient and stable non-precious metal catalysts for catalytic ammonia synthesis and ammonia cracking to hydrogen. The general principles developed here will have broad relevance to industrially important catalytic reactions involving catalyst restructuring. Databases and AI/ML workflows will be made publicly available to enable use of research products by the catalyst materials community. 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.