Automated assignment of rotational spectra using artificial neural networks
Zaleski, Daniel P.
Loading…
Permalink
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
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.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
