Uncertainty quantification in multiscale stochastic models of catalytic reactions

Vlachos, Dionisios G.Multiscale modeling, a key tool in probing the fundamentals of catalytic reactions, has seen increased usage enabled by advances in computational hardware. Within the multiscale modeling paradigm, kinetic Monte Carlo (KMC) is employed to simulate chemical reaction networks, as mean-eld models often fail to provide a meaningful description of the complex phenomena involved. Due to KMC's high computational cost and stochastic noise, quantifying uncertainty for the purposes of rening the model and assessing predictive reliability is dicult. Uncertainty arises from errors in input parameters (parametric uncertainty) and assumptions made about the physical system (model form uncertainty). ☐ In this thesis, we develop tools to quantify errors from each of the aforementioned sources and make recommendations for model renement. We address parametric uncertainty by developing ecient sensitivity analysis techniques, which identify the most influential parameters. Likelihood ratio sensitivity analysis (LRSA) computes all sensitivities without the need for additional runs, as required by nite dierence methods, but encounters tremendous variance in systems with disparate time scales. To overcome this limitation of LRSA, we derive mathematical theory that enables its use in well-mixed multiscale KMC and implement the method in original software. The new multiscale technique accurately computes sensitivities in a model system for which the traditional LRSA performs poorly. To address spatial KMC, we develop acceleration techniques and statistical criteria that ensure sucient sampling for LRSA. As a result, LRSA can be applied to real chemistry. We apply our methodology to the water-gas shift reaction on Pt, an important component of hydrogen production from biomass. We address model form uncertainty by revisiting two common assumptions: the structure of the catalyst surface is uniform and the identity of the active site is known. ☐ A framework for optimizing catalyst structure based on local descriptors is developed, allowing for atom-by-atom design of defected surfaces and consequent improvements in activity. In order to restrict our search to physically relevant structures, surface energy is also computed. Activity is maximized and surface energy is minimized simultaneously using multi-objective simulated annealing. A set of Pareto optimal structures is found, oering targets for synthesis. We apply our approach to oxygen reduction on Pt, the key reaction in automotive fuel cells. Our approach resolves discrepancy between experiment and theory regarding the extent to which defects can improve activity. We extend the approach to chemistries involving coupled active sites, for which KMC simulation is needed. KMC simulation data from many dierent structures is used to train a neural network for use as a surrogate model in the optimization. The neural network is updated as the optimization progresses in an online machine learning approach. In doing so, geometric eects such as diusion limitations and bifunctional site coupling are accurately captured within the structure optimization. The impact on the optimal structure is analyzed, yielding new insights into catalyst structure/activity relationships.University of Delaware, Department of Chemical and Biomolecular EngineeringPh.D