Invention Grant
- Patent Title: Identification and assignment of rotational spectra using artificial neural networks
-
Application No.: US15936329Application Date: 2018-03-26
-
Publication No.: US11380422B2Publication Date: 2022-07-05
- Inventor: Kirills Prozuments , Daniel P. Zaleski
- Applicant: UChicago Argonne, LLC
- Applicant Address: US IL Chicago
- Assignee: UChicago Argonne, LLC
- Current Assignee: UChicago Argonne, LLC
- Current Assignee Address: US IL Chicago
- Agency: Marshall, Gerstein & Borun LLP
- Main IPC: G16C20/30
- IPC: G16C20/30 ; G16C20/20 ; G16C20/70 ; G06N3/04 ; G06N3/08

Abstract:
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.
Public/Granted literature
- US20190294757A1 IDENTIFICATION AND ASSIGNMENT OF ROTATIONAL SPECTRA USING ARTIFICIAL NEURAL NETWORKS Public/Granted day:2019-09-26
Information query