Automated assignment of rotational spectra using artificial neural networks
Abstract
Here, a typical broadband rotational spectrum may contain several thousand observable transitions, spanning many species. While these spectra often encode troves of chemical information, identifying and assigning the individual spectra can be challenging. Traditional approaches typically involve visually identifying a pattern. A more modern approach is to apply an automated fitting routine. In this approach, combinations of 3 transitions are searched by trial and error, to fit the A, B, and C rotational constants in a Watson-type Hamiltonian. In this work, we develop an alternative approach-to utilize machine learning to train a computer to recognize the patterns inherent in rotational spectra. Broadband high-resolution rotational spectra are perhaps uniquely suited for pattern recognition, assignment, and species identification using machine learning. Repeating patterns of transition frequencies and intensities are now routinely recorded in broadband chirped-pulse Fourier transform microwave experiments in which both the number of resolution elements and the dynamic range surpass 104. At the same time, these high-resolution spectra are extremely sensitive to molecular geometry with each polar species having a unique rotational spectrum. Here we train the feed forward neural network on thousands of rotational spectra that we calculate, using the rules of quantum mechanics, from randomly generated sets ofmore »
- Authors:
-
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences, and Biosciences Division
- OSTI Identifier:
- 1475065
- Alternate Identifier(s):
- OSTI ID: 1470596
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Chemical Physics
- Additional Journal Information:
- Journal Volume: 149; Journal Issue: 10; Journal ID: ISSN 0021-9606
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Zaleski, Daniel P., and Prozument, Kirill. Automated assignment of rotational spectra using artificial neural networks. United States: N. p., 2018.
Web. doi:10.1063/1.5037715.
Zaleski, Daniel P., & Prozument, Kirill. Automated assignment of rotational spectra using artificial neural networks. United States. https://doi.org/10.1063/1.5037715
Zaleski, Daniel P., and Prozument, Kirill. Thu .
"Automated assignment of rotational spectra using artificial neural networks". United States. https://doi.org/10.1063/1.5037715. https://www.osti.gov/servlets/purl/1475065.
@article{osti_1475065,
title = {Automated assignment of rotational spectra using artificial neural networks},
author = {Zaleski, Daniel P. and Prozument, Kirill},
abstractNote = {Here, a typical broadband rotational spectrum may contain several thousand observable transitions, spanning many species. While these spectra often encode troves of chemical information, identifying and assigning the individual spectra can be challenging. Traditional approaches typically involve visually identifying a pattern. A more modern approach is to apply an automated fitting routine. In this approach, combinations of 3 transitions are searched by trial and error, to fit the A, B, and C rotational constants in a Watson-type Hamiltonian. In this work, we develop an alternative approach-to utilize machine learning to train a computer to recognize the patterns inherent in rotational spectra. Broadband high-resolution rotational spectra are perhaps uniquely suited for pattern recognition, assignment, and species identification using machine learning. Repeating patterns of transition frequencies and intensities are now routinely recorded in broadband chirped-pulse Fourier transform microwave experiments in which both the number of resolution elements and the dynamic range surpass 104. At the same time, these high-resolution spectra are extremely sensitive to molecular geometry with each polar species having a unique rotational spectrum. Here we train the feed forward neural network on thousands of rotational spectra that we calculate, using the rules of quantum mechanics, from randomly generated sets of rotational constants and other Hamiltonian parameters. Reasonable physical constraints are applied to these parameter sets, yet they need not belong to existing species. A trained neural network presented with a spectrum identifies its type (e.g., linear molecule, symmetric top, or asymmetric top) and infers the corresponding Hamiltonian parameters (rotational constants, distortion, and hyperfine constants). The classification and prediction times, about 160 μs and 50 μs, respectively, seem independent of the spectral complexity or the number of molecular parameters. We describe how the network works, provide benchmarking results, and discuss future directions.},
doi = {10.1063/1.5037715},
journal = {Journal of Chemical Physics},
number = 10,
volume = 149,
place = {United States},
year = {Thu Sep 13 00:00:00 EDT 2018},
month = {Thu Sep 13 00:00:00 EDT 2018}
}
Web of Science
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Automated, context-free assignment of asymmetric rotor microwave spectra
journal, May 2019
- Yeh, Lia; Satterthwaite, Lincoln; Patterson, David
- The Journal of Chemical Physics, Vol. 150, Issue 20
Automated, Context-Free Assignment of Asymmetric Rotor Microwave Spectra
conference, June 2019
- Yeh, Lia; Patterson, David; Satterthwaite, Lincoln
- Proceedings of the 74th International Symposium on Molecular Spectroscopy