Deep Representation Learning Of Spectroscopic Graphs

Lee, Kelvin

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https://hdl.handle.net/2142/111125 Copy

Description

Title

Deep Representation Learning Of Spectroscopic Graphs

Author(s)

Lee, Kelvin

Contributor(s)

• Crabtree, Kyle N.
• McGuire, Brett A.
• Li, Christine

Issue Date

2021-06-25

Keyword(s)

Spectroscopy as an analytical tool

Abstract

Graph representations of spectroscopic information have been increasing in popularity due to their efficiency and scalability in encoding large volumes of data; simply, energy levels are represented as nodes, and transitions as edges between each level. Thus, all quantum mechanical information pertaining to a molecule can be readily manipulated and transformed using a single data format, allowing computations and visualizations to be performed with ease. One application of spectroscopic graphs is to assist in the analysis of high resolution spectra of complex mixtures, comprising many observed transitions from an unknown number of molecules. Analysis of such mixtures comprises two coupled tasks: assignment of features to their respective signal carriers, and to decompose the full spectrum into known molecules. Viewing this problem from the perspective of spectroscopic graphs, our task is to fully reconstruct the subgraphs (i.e. molecules) from sparse information obtained with techniques such as AMDOR and MST. Here, we apply the use of deep graph learning techniques for spectroscopic graph reconstruction. Using convolutional autoencoder architectures, we experiment with the possibility of parameterizing graph neural networks to reproduce complex graphs when given only a limited number of ``observed'' energy levels and/or transitions. As part of this work, we perform a comprehensive investigation into the successes and challenges of our approach, including the interpretability of learned representations, how they can be manipulated and analyzed, and its applicability in complex mixture analysis.

Publisher

International Symposium on Molecular Spectroscopy

Type of Resource

Text

Language

eng

Permalink

http://hdl.handle.net/2142/111125

DOI

10.15278/isms.2021.FF09

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