(YIP) STOCHASTIC METHODS FOR CARBON DIOXIDE CATALYSIS
Abstract
For nearly a century, the holy grail of theoretical chemistry has been to develop methods capable of predicting reactions long before ever setting foot in a lab. While extraordinary progress has been made in this direction, using simulations to design new molecular catalysts or to even predict the mechanisms of known catalysts remains an outstanding challenge with profound societal implications. In this work, we propose to harness and build upon the accuracy and increasing speed of Auxiliary Field Quantum Monte Carlo, a cutting edge stochastic electronic structure method, to model homogeneous transition metal catalysts that reduce carbon dioxide into useful synthetic precursors. The development of high affinity, high selectivity carbon dioxide catalysts would transform our chemical economy, enabling the low-cost production of fuels and chemicals virtually anywhere on the planet, to the immediate benefit of the Air Force. Even so, little is quantitatively known about the mechanisms that underlie existing molecular catalysts, making their systematic improvement challenging. We aim to develop highly accurate, comparatively low-scaling methods that will enable more rapid and reliable screening of catalysts, while also holding the promise of being able to model a wide range of molecules and solids currently beyond the reach of deterministic methods.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2021
- Source ID
- FA95502010041
Entities
People
- Brenda Rubenstein
Organizations
- Air Force Office of Scientific Research
- Brown University
- United States Air Force