The emergence of advanced computational techniques and artificial intelligence has
strongly impacted the materials discovery and optimization. This study focuses on
applying computational methods to extract information from complex spectral systems.
Three distinct tiers of information extraction from hyperspectral data are explored: integrating
light data treatment with computational modeling, employing convolutional
neural networks for signal reconstruction, and advancing quantification using probabilistic
machine learning.
In the first tier, utilizing electron energy loss spectroscopy (EELS) in conjunction with
boundary element method modeling, we uncovered the broadband plasmonic properties
in wrinkled gold structures and their origin. We demonstrated the link between broadband
plasmonic characteristics and surface nano-features, offering insights into property
tunability.
To benefit the broader microscopy community, in the second tier, we developed EELSpecNet,
a Python script based on convolutional neural networks. EELSpecNet reconstructs
signals to retrieve details that were obscured by various signal artifacts. EELSpecNet
was benchmarked for near-zero-loss EELS, a challenging signal that contains
crucial phononic and plasmonic information. The results clearly show that this neural
network approach surpasses conventional Bayesian methods in deconvolution, particularly
in terms of information retrieval, signal fidelity, and noise reduction.
The final tier of this research introduces an innovative approach to spectral analysis
and quantification using probabilistic machine learning methods. By employing
the Markov Chain Monte Carlo sampling and Gaussian Process Regression models, this
tool facilitates spectral quantifications, provides comprehensive uncertainty analysis, reduces
human biases in the decision-making and model selection processes. This tool is
particularly useful for in-operando X-ray diffraction data analysis, a key technique for
examining battery materials. This method effectively disentangles overlapping peaks,
quantifies each peak, and tracks their evolution. Tested on both synthetic and real
experimental data, the tool demonstrated its efficacy and versatility. Given its broad
adaptability, this method is suitable for a variety of spectroscopy techniques. / Thesis / Doctor of Science (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29701 |
| Date | January 2024 |
| Creators | Mousavi Masouleh, Seyed Shayan |
| Contributors | Botton, Gianluigi, Materials Science and Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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