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Title:AUTOMATED ASSIGNMENT OF ROTATIONAL SPECTRA USING ARTIFICIAL NEURAL NETWORKS
Author(s):Zaleski, Daniel P.
Contributor(s):Prozument, Kirill
Subject(s):Mini-symposium: New Ways of Understanding Molecular Spectra
Abstract:Last year at this conference several approaches to utilize machine learning\footnote{Bishop, C M. “Neural networks for pattern recognition.” Oxford university press, 1995.} to train a computer to recognize the patterns inherit in rotational spectra were presented\footnote{Zaleski, D. P.; Prozument, K. Identifying Broadband Rotational Spectra with Neural Networks, International Symposium on Molecular Spectroscopy, June 21; 2017.}. It was shown that the recognized patterns could be used to identify (or classify) a rotational spectrum by its Hamiltonian type, but at the time, the rotational constants were not recovered. Here, we describe a feed forward artificial neural network that has been trained to identify different types of rotational spectra and determine the parameters of the molecular Hamiltonians. The network requires no user interaction beyond loading a “peak pick”, and can return fits within a fraction of a second. The rotational constants are typically deduced with the accuracy of 1–10 MHz. We will describe how the network works and provide benchmarking results.
Issue Date:06/19/18
Publisher:International Symposium on Molecular Spectroscopy
Citation Info:APS
Genre:Conference Paper / Presentation
Type:Text
Language:English
URI:http://hdl.handle.net/2142/100653
DOI:10.15278/isms.2018.TH01
Other Identifier(s):TH01
Date Available in IDEALS:2018-08-17
2018-12-12


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