Theoretical and Computational Chemistry

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

Abstract

Kinetic parameters for surface reactions can be predicted using a combination of DFT 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 eV 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.

Version notes

Authors' version after two rounds of reviewers' comments.

Content

Thumbnail image of LSRs in RMG-Cat preprint.pdf

Supplementary material

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supplementaryinfo

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