Building models of spectroscopy for condensed phase systems with atomistic detail using theory and machine learning
Abstract/Contents
- Abstract
- Spectroscopic techniques provide us with a means of investigating a system's microscopic structure and dynamics. Accurate atomistic simulations can help us explicitly connect spectroscopic features to the underlying electronic and nuclear structure and dynamics that give rise to them. In this dissertation, I highlight my work in rendering accurate atomistic simulations of different linear and multidimensional spectroscopies more computationally tractable by leveraging semiclassical approaches for theoretically treating spectroscopies and developing machine learning (ML) models to serve as proxies for ab initio electronic structure calculations. Chapter 1 provides a quick overview of the ML approaches I employed and theoretical background for how I used molecular dynamics (MD) simulations to simulate different spectroscopies. Chapter 2 presents work I have conducted in training ML potential energy surfaces for liquid water using transfer learning to target high-level ab initio electronic structure theories in order to accurately and efficiently conduct MD simulations. In Chapters 3 and 4, I develop ML models for electronic excitation energies in order to simulate linear and 2D electronic absorption spectroscopies for various solvated chromophore systems and highlight the inability of TDDFT to treat the extent to which hydrogen-bonding affects the distribution of excitation energies. Lastly, Chapter 5 highlights my work in developing a theoretical framework to simulate novel time-resolved X-ray diffraction experiments, which can be used to probe the orientational structural dynamics of disordered condensed phase systems, and benchmarking with results for liquid chloroform.
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 | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Chen, Michael Stephen |
|---|---|
| Degree supervisor | Markland, Thomas E |
| Thesis advisor | Markland, Thomas E |
| Thesis advisor | Kanan, Matthew William, 1978- |
| Thesis advisor | Rotskoff, Grant |
| Degree committee member | Kanan, Matthew William, 1978- |
| Degree committee member | Rotskoff, Grant |
| Associated with | Stanford University, School of Humanities and Sciences |
| Associated with | Stanford University, Department of Chemistry |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Michael Stephen Chen. |
|---|---|
| Note | Submitted to the Department of Chemistry. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/jg553nr2101 |
Access conditions
- Copyright
- © 2023 by Michael Stephen Chen
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...