(246a) Impact of Uncertainty Quantification and Density Functional Theory Error Propagation on Surface Catalyzed Kinetic Models
- Conference: AIChE Annual Meeting
- Year: 2019
- Proceeding: 2019 AIChE Annual Meeting
- Group: Topical Conference: Applications of Data Science to Molecules and Materials
- Time: Tuesday, November 12, 2019 - 8:00am-8:18am
Parametric uncertainty currently limits the predictive ability of kinetic models and prevents them from reaching their full potential. This is particularly important for surface catalyzed microkinetic models which often contain thousands of reaction steps and intermediate surface species requiring many kinetic and thermodynamic parameters. Estimating these from first-principles density functional theory (DFT) is costly, motivating the need for DFT-based methods, such as group additivity and BrÃ¸nstedâEvansâPolanyi (BEP) relationships, to quickly estimate thermochemistry and reaction barriers, respectively. Uncertainty in DFT and these methods is amplified by the Boltzmann factors in the reaction rate expressions increasing uncertainty in estimating the overall reaction rate. Furthermore, microkinetic model parameters are correlated; yet, there is lack of a framework to expose these correlations on a single catalyst with a single functional. This work develops a framework to estimate correlations in reaction networks and DFT uncertainty propagation. Uncertainty quantification (UQ) is then predicted for thermochemistry, reaction barriers, reaction path, and ultimately reaction rates. Propane combustion and ethane oxidative dehydrogenation reactions are illustrated. We will also demonstrate how to use the Python Multiscale Thermochemistry Toolbox, pMuTT , to calculate uncertainty in apparent activation energy.
 Python Multiscale Thermochemistry Toolbox (pMuTT), https://pypi.org/project/pMuTT/