자료유형  

 학위논문

Control Number  

0016933316

International Standard Book Number  

9798379909284

Dewey Decimal Classification Number  

530

Main Entry-Personal Name  

Tetef, Samantha.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Washington., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(314 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

General Note  

Advisor: Seidler, Gerald T.

Dissertation Note  

Thesis (Ph.D.)--University of Washington, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Data science and machine learning (ML) methods are revolutionizing scientific analysis and data processing. As a case in point, ML applied to X-ray spectroscopies has recently exploded, showcasing its effectiveness in fields such as electrical energy storage and chemical catalysis. Here, I include comprehensive computational studies of ML techniques applied to X-ray spectra, including X-ray absorption near edge structure (XANES) and valence-to-core X-ray emission spectra (VtC-XES). First, I utilize unsupervised ML to extract import chemical fingerprints and information content in sulforganics and phosphorganics, elucidating important and sometimes surprising correlations between spectral content and elemental coordination or electronic structure. In this work, I compare different unsupervised ML techniques, namely principal component analysis (PCA), variational autoencoder (VAE), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP), and I find important benefits from the added flexibility of similarity-based manifold mappings. Additionally, I help develop open-source tools for future researchers to utilize, including an API that interacts with PubChem to efficiently download and store metadata. Next, I use ML to improve the reliability of data analysis and decrease computational time in the context of XANES imaging experiments. To do so, I utilize dimensionality reduction and clustering to perform image segmentation and then identified phase composition using linear combination fitting. By decoupling the domain identification from the phase identification, I provide a more robust way to handle noise that was not reliant on obtaining appropriate linear combination fitting results. Moreover, my pipeline is flexible enough to uniquely incorporate auxiliary data or multimodal characterization measurements. Finally, I use feature selection to accelerate high-throughput experimental design. Specifically, I use Recursive Feature Elimination to select the most important energies in XANES spectra to measure in the context of a reference library. This approach is validated by appropriate analysis on these reduced-energy spectra using a nano-XANES image. All these approaches and tools are broadly applicable to X-ray spectroscopy on other chemical systems or and are also likely to find use in other spectroscopy techniques.

Subject Added Entry-Topical Term  

Physics.

Subject Added Entry-Topical Term  

Nanotechnology.

Subject Added Entry-Topical Term  

Optics.

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

X-ray spectroscopy

Index Term-Uncontrolled  

Variational autoencoder

Added Entry-Corporate Name  

University of Washington Physics

Host Item Entry  

Dissertations Abstracts International. 85-01B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:640050