A Lattice Kinetic Monte Carlo Solver for FirstPrinciples Microkinetic Trend Studies
Abstract
Here, meanfield microkinetic models in combination with Brønsted–Evans–Polanyi like scaling relations have proven highly successful in identifying catalyst materials with good or promising reactivity and selectivity. Analysis of the microkinetic model by means of lattice kinetic Monte Carlo promises a faithful description of a range of atomistic features involving shortrange ordering of species in the vicinity of an active site. In this paper, we use the “fruit fly” example reaction of CO oxidation on fcc(111) transition and coinage metals to motivate and develop a lattice kinetic Monte Carlo solver suitable for the numerically challenging case of vastly disparate rate constants. As a result, we show that for the case of infinitely fast diffusion and absence of adsorbateadsorbate interaction it is, in fact, possible to match the prediction of the meanfieldtheory method and the lattice kinetic Monte Carlo method. As a corollary, we conclude that lattice kinetic Monte Carlo simulations of surface chemical reactions are most likely to provide additional insight over meanfield simulations if diffusion limitations or adsorbate–adsorbate interactions have a significant influence on the mixing of the adsorbates.
 Authors:

 Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
 Publication Date:
 Research Org.:
 SLAC National Accelerator Lab., Menlo Park, CA (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC22)
 OSTI Identifier:
 1457053
 Grant/Contract Number:
 AC0276SF00515
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Journal of Chemical Theory and Computation
 Additional Journal Information:
 Journal Volume: 14; Journal Issue: 3; Journal ID: ISSN 15499618
 Publisher:
 American Chemical Society
 Country of Publication:
 United States
 Language:
 English
 Subject:
 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 97 MATHEMATICS AND COMPUTING
Citation Formats
Hoffmann, Max J., and Bligaard, Thomas. A Lattice Kinetic Monte Carlo Solver for FirstPrinciples Microkinetic Trend Studies. United States: N. p., 2018.
Web. doi:10.1021/acs.jctc.7b00683.
Hoffmann, Max J., & Bligaard, Thomas. A Lattice Kinetic Monte Carlo Solver for FirstPrinciples Microkinetic Trend Studies. United States. doi:10.1021/acs.jctc.7b00683.
Hoffmann, Max J., and Bligaard, Thomas. Mon .
"A Lattice Kinetic Monte Carlo Solver for FirstPrinciples Microkinetic Trend Studies". United States. doi:10.1021/acs.jctc.7b00683. https://www.osti.gov/servlets/purl/1457053.
@article{osti_1457053,
title = {A Lattice Kinetic Monte Carlo Solver for FirstPrinciples Microkinetic Trend Studies},
author = {Hoffmann, Max J. and Bligaard, Thomas},
abstractNote = {Here, meanfield microkinetic models in combination with Brønsted–Evans–Polanyi like scaling relations have proven highly successful in identifying catalyst materials with good or promising reactivity and selectivity. Analysis of the microkinetic model by means of lattice kinetic Monte Carlo promises a faithful description of a range of atomistic features involving shortrange ordering of species in the vicinity of an active site. In this paper, we use the “fruit fly” example reaction of CO oxidation on fcc(111) transition and coinage metals to motivate and develop a lattice kinetic Monte Carlo solver suitable for the numerically challenging case of vastly disparate rate constants. As a result, we show that for the case of infinitely fast diffusion and absence of adsorbateadsorbate interaction it is, in fact, possible to match the prediction of the meanfieldtheory method and the lattice kinetic Monte Carlo method. As a corollary, we conclude that lattice kinetic Monte Carlo simulations of surface chemical reactions are most likely to provide additional insight over meanfield simulations if diffusion limitations or adsorbate–adsorbate interactions have a significant influence on the mixing of the adsorbates.},
doi = {10.1021/acs.jctc.7b00683},
journal = {Journal of Chemical Theory and Computation},
number = 3,
volume = 14,
place = {United States},
year = {2018},
month = {1}
}
Web of Science