Computational optimal transport for molecular spectra: The fully continuous case

Author:

Seifert Nathan A.12ORCID,Prozument Kirill1ORCID,Davis Michael J.1ORCID

Affiliation:

1. Chemical Sciences and Engineering Division, Argonne National Laboratory 1 , Lemont, Illinois 60439, USA

2. Department of Chemistry and Chemical and Biomedical Engineering, University of New Haven 2 , 300 Boston Post Road, West Haven, Connecticut 06516, USA

Abstract

Computational optimal transport is used to analyze the difference between pairs of continuous molecular spectra. It is demonstrated that transport distances which are derived from this approach may be a more appropriate measure of the difference between two continuous spectra than more familiar measures of distance under many common circumstances. Associated with the transport distances is the transport map which provides a detailed analysis of the difference between two molecular spectra and is a key component of our study of quantitative differences between two continuous spectra. The use of optimal transport for comparing molecular spectra is developed in detail here with a set of model spectra, so that the discussion is self-contained. The difference between the transport distance and more common definitions of distance is elucidated for some well-chosen examples and it is shown where transport distances may be very useful alternatives to standard definitions of distance. The transport distance between a theoretical and experimental electronic absorption spectrum for SO2 is studied and it is shown how the theoretical spectrum can be modified to fit the experimental spectrum better adjusting the theoretical band origin and the resolution of the theoretical spectrum. This analysis includes the calculation of transport maps between the theoretical and experimental spectra suggesting future applications of the methodology.

Funder

Basic Energy Sciences

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Reference69 articles.

1. See https://en.wikipedia.org/wiki/Distance for Wikipedia: Distance.

2. Optimal mass transport: Signal processing and machine learning applications;IEEE Signal Process. Mag.,2017

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