Identifying broadband rotational spectra with neural networks

Zaleski, Daniel P.

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https://hdl.handle.net/2142/97003 Copy

Description

Title

Identifying broadband rotational spectra with neural networks

Author(s)

Zaleski, Daniel P.

Contributor(s)

Prozument, Kirill

Issue Date

2017-06-21

Keyword(s)

Spectroscopy as an analytical tool

Abstract

A typical broadband rotational spectrum may contain several thousand observable transitions, spanning many speciesfootnote{ Perez et al. “Broadband Fourier transform rotational spectroscopy for structure determination: The water heptamer.” Chem. Phys. Lett., 2013, 571, 1–15.}. Identifying the individual spectra, particularly when the dynamic range reaches 1,000:1 or even 10,000:1, can be challenging. One approach is to apply automated fitting routinesfootnote{Seifert et al. “AUTOFIT, an Automated Fitting Tool for Broadband Rotational Spectra, and_x000d_ Applications to 1-Hexanal.” J. Mol. Spectrosc., 2015, 312, 13–21.}. In this approach, combinations of 3 transitions can be created to form a “triple”, which allows fitting of the A, B, and C rotational constants in a Watson-type Hamiltonian. On a standard desktop computer, with a target molecule of interest, a typical AUTOFIT routine takes 2–12 hours depending on the spectral density. A new approach is to utilize machine learningfootnote{Bishop. “Neural networks for pattern recognition.” Oxford university press, 1995.} to train a computer to recognize the patterns (frequency spacing and relative intensities) inherit in rotational spectra and to identify the individual spectra in a raw broadband rotational spectrum. Here, recurrent neural networks have been trained to identify different types of rotational spectra and classify them accordingly. Furthermore, early results in applying convolutional neural networks for spectral object recognition in broadband rotational spectra appear promising._x000d_

Publisher

International Symposium on Molecular Spectroscopy

Type of Resource

text

Language

eng

Permalink

http://hdl.handle.net/2142/97003

DOI

https://doi.org/10.15278/isms.2017.WE08

Copyright and License Information

Copyright 2017 Daniel P. Zaleski

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