1. Catalysis Center for Energy Innovation Overview of Multiscale Modeling Approach Dion Vlachos Univ. of Delaware

2. Catalysis Center for Energy Innovation 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. Catalysis Center for Energy Innovation The 30,000 Miles Airview 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) Much less work has been done at the systems’ level

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

5. Catalysis Center for Energy Innovation 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 Brønsted Evans Polanyi (BEP) Microkinetic Model (MKM) 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) Linear Scaling Relations (LSRs) Group Additivity (GA)

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