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Chemistry

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Washington University in St. Louis

Theses/Dissertations

Catalysis

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Computational Design Of Two-Dimensional Transition Metal Dichalcogenide Alloys And Their Applications, John Douglas Cavin May 2021

Computational Design Of Two-Dimensional Transition Metal Dichalcogenide Alloys And Their Applications, John Douglas Cavin

Arts & Sciences Electronic Theses and Dissertations

The discovery of bronze as an alloy of copper and tin is arguably the earliest form of material design, dating back thousands of years. In contrast, two-dimensional materials are new to the 21st century. The research presented in this dissertation is at the intersection of alloying and two-dimensional materials. I specifically study a class of two-dimensional materials known as transition metal dichalcogenides (TMDCs). Because of the large number of transition metals, there are many combinations of TMDCs that can be alloyed, making experimental exploration of the phase space of possible alloys unwieldly. Instead, I have applied first-principles methods to study …


Oxidative C–C And C–Heteroatom Reactivity Of High-Valent Nickel Complexes, Sofia Marie Smith May 2019

Oxidative C–C And C–Heteroatom Reactivity Of High-Valent Nickel Complexes, Sofia Marie Smith

Arts & Sciences Electronic Theses and Dissertations

ABSTRACT OF THE DISSERTATION

Oxidative C–C and C–Heteroatom Reactivity of High-Valent Nickel Complexes

by

Sofia M. Smith

Doctor of Philosophy in Chemistry

Washington University in St. Louis, 2019

Professor Liviu M. Mirica, Chair

Professor Kevin D. Moeller, Co-Chair

Nickel catalysts are commonly used for cross-coupling reactions such as Negishi, Kumada and Suzuki couplings. While Ni(0), Ni(I), and Ni(II) intermediates are most relevant in these transformations, Ni(III) and Ni(IV) species have also been recently proposed to play a role in catalysis. The formation of C–C and C–heteroatom bonds plays a fundamental role in organic transformations, and today cross-coupling reactions are one …


Applying Bayesian Machine Learning Methods To Theoretical Surface Science, Shane Carr Dec 2015

Applying Bayesian Machine Learning Methods To Theoretical Surface Science, Shane Carr

McKelvey School of Engineering Theses & Dissertations

Machine learning is a rapidly evolving field in computer science with increasingly many applications to other domains. In this thesis, I present a Bayesian machine learning approach to solving a problem in theoretical surface science: calculating the preferred active site on a catalyst surface for a given adsorbate molecule. I formulate the problem as a low-dimensional objective function. I show how the objective function can be approximated into a certain confidence interval using just one iteration of the self-consistent field (SCF) loop in density functional theory (DFT). I then use Bayesian optimization to perform a global search for the solution. …