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Overview of Multiscale Modeling Approach

Overview of Multiscale Modeling Approach. Dion Vlachos Univ. of Delaware. Mathematical and computational methods developed Bottom-up modeling Process design Coarse-graining Top-down modeling Catalyst design. Bottom-up and Top-down Modeling: Process Design and Catalyst Screening.

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Overview of Multiscale Modeling Approach

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  1. Overview of Multiscale Modeling Approach Dion Vlachos Univ. of Delaware

  2. Mathematical and computational methods developed Bottom-up modeling Process design Coarse-graining Top-down modeling Catalyst design Bottom-up and Top-down Modeling:Process Design and Catalyst Screening Reviews: Chem. Eng. J. 90, 3 (2002); Chem. Eng. Sci. 59, 5559 (2004); Adv. Chem. Eng. 30, 1 (2005)

  3. The 30,000 Miles Airview Much less work has been done at the systems’ level Significant progress made on method development and testing Field is maturing Focus has been on prototype problems Complex systems have by-and-large not been studied Perspecive: Vlachos, AIChE J. 58(5), 1314 (2012)

  4. Hierarchy Enables Rapid Screening of Chemistry, Fuels, and Catalysts Length Accuracy, cost Reactor scale: Performance Ideal: PFR, CSTR, etc. Computational Fluid Dynamics (CFD) Pseudo-homogeneous: Transport correlations Catalyst scale: Reaction rate Continuum: MF-ODEs Mesoscopic: PDEs Discrete: CG-KMC Discrete: KMC Reaction network builder Uncertainty quantification Electronic scale: Parameter estimation Quantum-based correlations: BEPs, GA, LSRs Quantum: ab initio, DFT, TST, CPMD, QM/MM MD Hierarchy adds a new dimension to multiscaling: at each scale, more than one model can be run Review: Salciccioli et al., Chem. Eng. Sci. 66, 4319 (2011)

  5. Toward High-throughput Computing:Metal and Metal-like Catalysis Thermochemistry via GA & LSRs Reaction barriers and pre-exps via BEPs Perform MKM DFT-based, semi-empirical, or hierarchical (screen with semi-empirical and refine via DFT) Error analysis; Assessment of model predictions • Linear Scaling Relations (LSRs) Group Additivity (GA) • Brønsted Evans Polanyi (BEP) Microkinetic Model (MKM) Salciccioli et al., J. Phys. Chem. C , 114, 20155 (2010); J. Phys. Chem. C, 116, 1873 (2012) Sutton and Vlachos, ACS Catal.2, 1624 (2012); J. Catal. 297, 202 (2013)

  6. The Kinetic Monte Carlo Approach Metal surface products transition state Potential Energy Surface reactants CO(gas) + OH COOH Stamatakis and Vlachos, J. Chem. Phys. 134, 214115 (2011); http://www.dion.che.udel.edu/downloads.php Instead of simulating dynamics, KMC focuses on rare events Simulates reactions much faster than Molecular Dynamics Incorporates spatial information contrary to micro-kinetic models

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