Adatom density kinetic Monte Carlo: A hybrid approach to perform epitaxial growth simulations

Mandreoli, Lorenzo; Neugebauer, Jörg; Kunert, R.; Schöll, Eckehard

doi:10.1103/physrevb.68.155429

Cited by 32 publications

(21 citation statements)

References 22 publications

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“…The computational time can also be reduced without significant loss of accuracy in the KMC simulation results by decoupling the predominant kinetic events and treating them deterministically. 65,66 This approach is most practical for fast kinetic events, e.g., surface diffusion, which may account for the majority of the KMC computational time. However, this approach is not applicable for the present multiscale reactor model, since the adsorption of species A and B are the surface events that consume most of the computational cost in the KMC simulation, i.e., roughly 28% for each species adsorption.…”

Section: Lattice Size Determination and Solution Approachmentioning

confidence: 99%

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

2016

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in Wiley Online Library (wileyonlinelibrary.com) Multiscale models have been developed to simulate the behavior of spatially-heterogeneous porous catalytic flow reactors, i.e., multiscale reactors whose concentrations are spatially-dependent. While such a model provides an adequate representation of the catalytic reactor, model-plant mismatch can significantly affect the reactor's performance in control and optimization applications. In this work, power series expansion (PSE) is applied to efficiently propagate parametric uncertainty throughout the spatial domain of a heterogeneous multiscale catalytic reactor model. The PSE-based uncertainty analysis is used to evaluate and compare the effects of uncertainty in kinetic parameters on the chemical species concentrations throughout the length of the reactor. These analyses reveal that uncertainty in the kinetic parameters and in the catalyst pore radius have a substantial effect on the reactor performance. The application of the uncertainty quantification methodology is illustrated through a robust optimization formulation that aims to maximize productivity in the presence of uncertainty in the parameters.

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Section: Lattice Size Determination and Solution Approachmentioning

confidence: 99%

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

2016

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“…Others introduce fluctuations into continuum methods [11]. Still others attempt to derive fully coarse grained equations from atomistic model by treating probability densities [12][13][14]. These methods have shown some considerable success in grappling with the complexity of coupling these two extremely different simulation schemes.…”

Section: Introductionmentioning

confidence: 98%

Multiscale diffusion Monte Carlo simulation of epitaxial growth

Chou

Falk

2006

Journal of Computational Physics

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“…There has been some effort to include shot noise fluctuations in a continuum model [25,26,27,28,29]. Most of these methods utilize a hybrid approach where adatoms are modeled as a continuous density, but islands are composed of discrete atoms.…”

Section: Continuum Modelsmentioning

confidence: 99%

Multiscale kinetic Monte Carlo algorithm for simulating epitaxial growth

2005

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We present a fast Monte-Carlo algorithm for simulating epitaxial surface growth, based on the continuous-time Monte-Carlo algorithm of Bortz, Kalos and Lebowitz. When simulating realistic growth regimes, much computational time is consumed by the relatively fast dynamics of the adatoms. Continuum and continuum-discrete hybrid methods have been developed to approach this issue; however in many situations, the density of adatoms is too low to efficiently and accurately simulate as a continuum. To solve the problem of fast adatom dynamics, we allow adatoms to take larger steps, effectively reducing the number of transitions required. We achieve nearly a factor of ten speed up, for growth at moderate temperatures and large D/F .

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Adatom density kinetic Monte Carlo: A hybrid approach to perform epitaxial growth simulations

Cited by 32 publications

References 22 publications

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

Multiscale diffusion Monte Carlo simulation of epitaxial growth

Multiscale kinetic Monte Carlo algorithm for simulating epitaxial growth

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