Automated Mechanism Generation Using Linear Scaling Relationships and Sensitivity Analyses Applied to Catalytic Partial Oxidation of Methane

Kinetic
parameters for surface reactions can be predicted using
a combination of density functional theory calculations, scaling relations,
and machine learning algorithms; however, construction of microkinetic
models still requires a knowledge of all the possible, or at least
reasonable, reaction pathways. The recently developed reaction mechanism
generator (RMG) for heterogeneous catalysis, now included in RMG version
3.0, is built upon well-established, open-source software that can
provide detailed reaction mechanisms from user-supplied initial conditions
without making a priori assumptions. RMG is now able
to estimate adsorbate thermochemistry and construct detailed microkinetic
models on a range of hypothetical metal surfaces using linear scaling
relationships. These relationships are a simple, computationally efficient
way to estimate adsorption energies by scaling the energy of a calculated
surface species on one metal to any other metal. By conducting simulations
with sensitivity analyses, users can not only determine the rate-limiting
step on each surface by plotting a “volcano surface”
for the degree of rate control of each reaction as a function of elemental
binding energies but also screen novel catalysts for desirable properties.
We investigated the catalytic partial oxidation of methane to demonstrate
the utility of this new tool and determined that an inlet gas C/O
ratio of 0.8 on a catalyst with carbon and oxygen binding energies
of −6.75 and −5.0 eV, respectively, yields the highest
amount of synthesis gas. Sensitivity analyses show that while the
dissociative adsorption of O2 has the highest degree of
rate control, the interactions between individual reactions and reactor
conditions are complex, which result in a dynamic rate-limiting step
across differing metals.