Identification and assignment of rotational spectra using artificial neural networks

Patent · Tue Jul 05 00:00:00 EDT 2022

OSTI ID:1924874

Prozuments, Kirills; Zaleski, Daniel P.

A method of identifying molecular parameters may include receiving observed transition frequencies, generating transition frequency sets and a spectral parameter sets, training one or more artificial neural networks by analyzing the transition frequency sets and the spectral parameter sets, analyzing the observed transition frequencies using the one or more trained artificial neural networks to predict estimated spectral parameters, and identifying molecular parameters by analyzing the estimated spectral parameters. A molecular parameter identification system may include a rotational spectrometer, a user interface, and a spectrum analysis application that may retrieve observed transition frequencies, identify a Hamiltonian type by a neural network analyzing the observed transition frequencies, select a second trained artificial neural network based on the identified Hamiltonian type, analyze observed transition frequencies using the second artificial neural network to identify estimated spectral parameters, and identify molecular parameters.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC02-06CH11357
• Assignee: UChicago Argonne, LLC (Chicago, IL)
• Patent Number(s): 11,380,422
• Application Number: 15/936,329
• OSTI ID: 1924874
• Country of Publication: United States
• Language: English