Automated Mixture Analysis via Structural Evaluation
Name
2408.15819v1.pdf
Description
Accepted version
Size
1.36 MB
Format
Adobe PDF
Checksum (MD5)
845b28b7406158779724de92ea571ba9
Author(s)
Fried, Zachary TP
McGuire, Brett A
Date Issued
September 12, 2024
Journal
The Journal of Physical Chemistry A
Publisher
American Chemical Society
Citation
Zachary T.P. Fried, Brett A. McGuire; Automated Mixture Analysis via Structural Evaluation. J. Phys. Chem. A 26 September 2024; 128 (38): 8254–8264.
Version
Author's final manuscript
Abstract
The determination of chemical mixture components is vital to a multitude of scientific fields. Oftentimes spectroscopic methods are employed to decipher the composition of these mixtures. However, the sheer density of spectral features present in spectroscopic databases can make unambiguous assignment to individual species challenging. Yet, components of a mixture are commonly chemically related due to environmental processes or shared precursor molecules. Therefore, analysis of the chemical relevance of a molecule is important when determining which species are present in a mixture. In this paper, we combine machine-learning molecular embedding methods with a graph-based ranking system to determine the likelihood of a molecule being present in a mixture based on the other known species and/or chemical priors. By incorporating this metric in a rotational spectroscopy mixture analysis algorithm, we demonstrate that the mixture components can be identified with extremely high accuracy (≥97%) in an efficient manner.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acs.jpca.4c03580
