Atomically dispersed amorphous catalysts: ab initio computational tools for a new frontier

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

The project aims to develop and apply computational methods to understand the catalytic properties of isolated metal atom sites on amorphous support materials. The computational results will be compared to experimental data on the Phillips Petroleum ethylene polymerization catalyst which has been a workhorse industrial catalyst for 60 years despite longstanding questions about the active sites and the mechanism. The results will not only provide information specific to potential improvements in the Phillips catalyst, but will improve theoretical tools for understanding a broad class of catalysts where the activity is dominated by a small fraction of highly active metal sites on the amorphous support. Related educational and outreach programs will be offered to students at all levels, including a game to engage high school students in scientific pursuits. The study will develop computational techniques to identify the specific properties that make certain catalytic sites highly active relative to others among an ensemble of isolated metal sites on an amorphous support material. The work specifically focuses on chromium supported on amorphous silica (the Phillips catalyst) for which a broad body of characterization data is available. The computational approach will combine machine learning techniques and rare events methods for analyzing the distribution of sites to predict relationships between structure and activity. Specifically, machine learning methods trained by ab initio calculations will learn how activity is related to the local structural environments of the isolated chromium species on the silica surface. Non-Boltzmann sampling techniques will ensure that rare but important sites with unusually high activities (low activation energies) are adequately sampled to ensure accurate site-averaged kinetic properties. The combined approach provides a new "importance learning" strategy that can be broadly used to build models of active site distributions, identify critical characteristics of highly active sites, and engineer better atomically dispersed catalysts on amorphous supports. Broader educational and outreach contributions include the development and public sharing of "plug-ins" that support the importance learning approach, virtual reality visualization of the machine learning tools, and a related game for high school students in which they will have an opportunity to compete with the machine learning algorithm to design highly active catalytic sites.