Gas-phase Infrared Spectra Analysis Via Deep Neural Networks

Enders, Abigail A.

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https://hdl.handle.net/2142/116653 Copy

Description

Title

Gas-phase Infrared Spectra Analysis Via Deep Neural Networks

Author(s)

Enders, Abigail A.

Contributor(s)

• Allen, Heather C.
• North, Nicole

Issue Date

2022-06-22

Keyword(s)

Mini-symposium: Machine Learning

Abstract

Infrared spectroscopy provides unique molecular vibrational information that is molecule and environment specific. Spectral responses, as images rather than array-based data, were used to train a deep neural network to develop analytical methods capable of large-scale information processing. We label spectra based on the present and absent functional groups, but the model must determine the frequencies, peak shape, and variability of each molecular response to identify functional groups. The resultant machine learning models significantly reduce the time required for traditional infrared spectral analysis and the functional group assignments is found to be more accurate than expert chemists. Application of machine learning methods to spectroscopic data is made approachable by a straightforward model system that is generalizable, broad, and well-performing on thousands of gas-phase infrared spectra from the NIST spectral database. Future improvement will involve more specific and applied models, such as the investigation of field samples for environmental contaminants or component identification, with increased solvent complexity to continue developing a broad range of models. To the best of our knowledge, this is the first presentation of a generalizable machine learning model for infrared analysis because it is capable of analyzing a diverse spectral database.

Publisher

International Symposium on Molecular Spectroscopy

Type of Resource

text

Language

eng

Handle URL

https://hdl.handle.net/2142/116653

DOI

https://doi.org/10.15278/isms.2022.WK06

Copyright and License Information

Copyright 2022 held by the authors

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