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

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.