Computational optimal transport for molecular spectra: The fully discrete case

Seifert, Nathan A.; Prozument, Kirill; Davis, Michael J.

doi:10.1063/5.0069681

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Computational optimal transport for molecular spectra: The fully discrete case

Seifert, Nathan A.·Prozument, Kirill·Davis, Michael J.

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Abstract

In this study, the use of computational optimal transport is investigated as a tool for comparing two molecular spectra. Unlike other techniques for comparing molecular spectra in a pattern-recognition framework, transport distances simultaneously encode information about line positions and intensities. In addition, it is shown that transport distances are a useful alternative to Euclidean distances as Euclidean distances are based on line-by-line comparisons, while transport distances reflect broader features of molecular spectra and adequately compare spectra with different resolutions. This paper includes a tutorial on the use of optimal transport and investigates several well-chosen examples to illustrate the utility of computational optimal transport for comparing molecular spectra.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY artificial intelligence artificial neural networks atomic and molecular spectra atomic and molecular spectroscopy comparators machine learning non-Euclidean geometry probability theory regression analysis rotational spectra

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Seifert, Nathan A., Prozument, Kirill, Davis, Michael J.. 2021-11-08. Computational optimal transport for molecular spectra: The fully discrete case. https://doi.org/10.1063/5.0069681

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