Automated assignment of rotational spectra using artificial neural networks
Zaleski, Daniel P.
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1195686.pptx
3258.pdf
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https://hdl.handle.net/2142/100653
Description
Title
Automated assignment of rotational spectra using artificial neural networks
Author(s)
Zaleski, Daniel P.
Contributor(s)
Prozument, Kirill
Issue Date
2018-06-19
Keyword(s)
Mini-symposium: New Ways of Understanding Molecular Spectra
Date of Ingest
2018-08-17T16:09:26Z
2018-12-12T22:35:43Z
Abstract
Last year at this conference several approaches to utilize machine learninga to train a computer to recognize the patterns inherit in rotational spectra were presentedb . 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.
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1195686.pptx
3258.pdf
