Insights into chemical reactions at interfaces from enhanced sampling and global optimization algorithms
Abstract/Contents
- Abstract
- Many important technologies and current scientific challenges involve chemical reactions that occur in complex environments, such as surfaces/interfaces or condensed phases which have complex structures and dynamics under reaction conditions. Engineering catalysts and processes for these reactions depends on developing an understanding of the mechanisms that determine the rates of these reactions. Applying computational modeling to catalytic reactions is challenging for several reasons; my work demonstrates several strategies for modeling increasingly complex chemical reactions. Accurate theoretical study of catalytic interfaces and solvent effects has been an active area of research for decades, as it requires simultaneously including detailed electronic structure and chemisorption effects from the catalyst and the many intermolecular interactions and long-range electrostatic interactions from the solvent, electrolyte or other surrounding environment, all of which must be sampled over an ensemble of configurations. I address this challenge by systematically applying global optimization algorithms to models of solvent-metal interfaces. I explain trends in adsorbate-electrolyte interactions and relate these to the adsorbate dipole moment and hydrogen bonding affinity, and shed new light on models of ion promotion effects. And I extend these methods to study the interfaces of nonaqueous solvents with transition metals, uncovering several new insights into the effects of solvent chemisorption on a variety of metal surfaces. The effect of temperature on the reaction free energies and barriers of elementary chemical reactions on surfaces is often calculated using the harmonic approximation. This is due to the computational expense of first principles calculations and the simplicity of the harmonic approximation. Often these methods rely on system-specific intuition and assumptions regarding which configurations and types of anharmonicities may be important. More rigorous, scalable and efficient free energy and enhanced sampling methods would be useful in addressing this challenge. In this thesis I describe applying a state-of-the art machine learning based enhanced sampling method to a density functional theory (DFT) model of a prototypical surface reaction. This method calculates the free energy profile of the reaction more efficiently (fewer calculations required) than other currently widely used free energy methods, while exploring and identifying the critical states in the reaction mechanism. I also apply metadyamics to study nitrogen dissociation in lithium, demonstrating the use of an enhanced sampling algorithm for the exploration of reaction mechanisms and calculation of rate constants of a surface reaction. This work is a step toward systematic, generalizable methods for the computational study of chemical reactions in complex environments.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ludwig, Thomas Kris |
|---|---|
| Degree supervisor | Noerskov, Jens |
| Degree supervisor | Qin, Jian, (Professor of Chemical Engineering) |
| Thesis advisor | Noerskov, Jens |
| Thesis advisor | Qin, Jian, (Professor of Chemical Engineering) |
| Thesis advisor | Cargnello, Matteo |
| Degree committee member | Cargnello, Matteo |
| Associated with | Stanford University, Department of Chemical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Thomas Ludwig. |
|---|---|
| Note | Submitted to the Department of Chemical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/xs855tv8159 |
Access conditions
- Copyright
- © 2021 by Thomas Kris Ludwig
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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