Computational efforts towards efficient catalyst design in the space of oxygen electrochemistry

Solid-state heterogeneous catalysts enable many of the predominant chemical reactions that drive modern society, and are critical in the chemical commodities, agriculture, and energy industries. Electrocatalysis is an important subclass of catalysis which utilizes the potential difference at electrode surfaces to drive electron transfer reactions to carry out chemical transformations. Of particular interest are the electrochemical reactions involving oxygen and water, the oxygen evolution reaction (OER) and the oxygen reduction reaction (ORR) which have applications in energy technologies such as fuel cell vehicles and the electrochemical production of hydrogen, a potential alternative to fossil fuel derived hydrogen fuel. The existential crisis presented by climate change compels us to decarbonize our energy infrastructure and phase out fossil fuels. Although these green electrochemical pathways, and the catalysts that drive them, have been well studied for decades, they have yet to become economically viable to compete with fossil fuels. One promising remedy is to increase the efficiency of these processes by developing new catalyst materials, and much effort has been expended in this area. Theoretical electronic structure simulations, via density functional theory, have become indispensable tools to accelerate the design of new catalyst materials by allowing for atomistic understanding of material properties. In the first part of this thesis, we demonstrate the use DFT simulations in modeling and understanding experimental catalyst systems. First, we investigate a two-dimensional metal-organic framework consisting of various metal centers (M) and a hexaaminobenzene functional unit (HAB) known as M-HAB for the ORR. Experimental and theoretical catalytic activity results for M-HAB are presented and are shown to be consistent with one another. Additionally, we show that the DFT simulations support a linker-mediated active site instead of a metal-center active site. Next, we present experiment-theory results on the OER behavior of doped iridium-oxide thin-film catalysts and thin-film catalysts of meta-stabilized Columbite IrO2. For the doped-IrO2 catalyst system, we help elucidate the role of the dopant atom on the binding of OER intermediate species. Simulations of the columbite-IrO2 system explore the facet dependence of catalytic activity and atomistic structural factors that account for this difference. In the last couple of chapters of this work, we explore machine learning and high-throughput workflow methods to continue our investigation into iridium-oxide catalysts for the OER. First, we report an active learning based crystal structure prediction algorithm for the purpose of efficiently finding stable polymorphic phases of IrO2 and IrO3. We show that IrOx polymorphs predominately adopt octahedral coordination motifs and we demonstrate that the more oxidized IrO3 stoichiometries exhibit elevated activity over the more conventional IrO2. Lastly, we extend the polymorph discovery story by reporting on a high-throughput OER dataset composed of IrOx surface slab models created from the bulk polymorphs of the previous section. We report the theoretical OER activity of a structurally diverse set of 500 IrOx surfaces and show that the variation in OER adsorption energies can be readily modeled with structurally derived features, including a coordination based effective oxidation state descriptor which can be readily calculated for unrelaxed surfaces

Keywords: Chemical Engineering.
Advisor: Bao Zhenan
Noerskov Jens
Jaramillo Thomas Francisco