Multiscale First Principles Reaction Discovery for Methane Pyrolysis
Computational reaction discovery in complex chemical systems has been an important research topic. Traditionally, researchers have adopted a hypothesis-driven approach, which relies on chemical intuition to guide reaction discovery. Advances in computational chemistry have led to a new era for exploring chemical systems in a hypothesis-free manner. One of the promising approaches is to leverage ab initio molecular dynamics (AIMD), where the electronic Schrodinger equation is solved to compute interatomic forces of freely reacting molecules and simulate bond rearrangements appropriately. In this presentation, I will demonstrate a state-of-the-art computational framework which is built upon our previously developed ab initio nanoreactor, a high-throughput graphical processing unit (GPU)-accelerated reaction discovery tool. We extended the nanoreactor reaction discovery from the molecular level (AIMD) to chemical kinetic modeling of methane pyrolysis under its combustion conditions. With the multiscale computation capability of combining AIMD with chemical kinetic modeling, we can accurately predict the evolution of the methane pyrolysis system over a much longer time (e.g., at order of seconds) than the nanosecond time scale that the nanoreactor AIMDs can access. This work provides a feasible path for future first-principles reaction discovery at higher throughput and larger scales.
Hosted by Professor Lu Wang
For Zoom link, visit remsinglab.com/pchemseminars.