Modeling the dynamical evolution of automotive three-way catalysts

Automobile catalytic converters reduce air pollution. They convert exhaust gases such as carbon monoxide, nitrogen oxides and unburned fuel into carbon dioxide and nitrogen. This conversion uses three-way catalysts (TWCs). TWCs contain precious metals such as platinum, palladium, and rhodium on a heat-resistant material. The global supply of precious metals is limited and demand continues to rise. One way to reduce the amount of precious metal is to spread it out as single atoms or tiny clusters. This also helps increase efficiency because more metal contacts exhaust gases. The project will model the behavior and performance of these advanced catalysts. The research will use computer-based tools such as quantum chemistry, molecular dynamics, and machine learning. The project will examine how different preparation methods affect catalyst stability. It will identify conditions that prevent damage over time, such as clumping of metal particles. These insights will help guide the design of more efficient and durable catalytic converters and may also benefit other fields, including corrosion prevention and crystal growth. In collaboration with an experimental expert in three-way catalysts (TWCs), this project will investigate the dynamics and energetics of key processes governing the stability of ceria-supported platinum and rhodium catalysts at low metal loadings, which are relevant to practical catalytic applications. Because high-fidelity quantum chemistry methods are typically restricted to small model systems and picosecond timescales due to their substantial computational cost, this effort will develop robust machine-learned interatomic potentials to enable simulations at experimentally relevant length and time scales. The transformative aspect of this work lies in its ability to simulate catalyst pretreatment and deactivation processes over accelerated timescales, allowing prediction of site distributions following pretreatment and direct comparison of competing deactivation mechanisms. Extensive configurational sampling through molecular dynamics simulations will be used to identify structural motifs and interfacial features that enhance resistance to sintering and other deactivation pathways, with these insights distilled into a general computational protocol for modeling pretreatment effects and advancing the understanding of complex phenomena such as strong metal–support interactions. The research will be integrated with educational initiatives, including summer research experiences and hands-on workshops designed to engage and train high school students in computational materials science. 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. NSF Award ID: 2542346 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Shaama Sharada | Institution: University of Southern California, LOS ANGELES, CA | Award Amount: $400,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2542346 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2542346.html