Identification and assignment of rotational spectra using artificial neural networks
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.
- Inventors:
- Issue Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1924874
- Patent Number(s):
- 11380422
- Application Number:
- 15/936,329
- Assignee:
- UChicago Argonne, LLC (Chicago, IL)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
G - PHYSICS G16 - INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS G16C - COMPUTATIONAL CHEMISTRY
- DOE Contract Number:
- AC02-06CH11357
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 03/26/2018
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Prozuments, Kirills, and Zaleski, Daniel P. Identification and assignment of rotational spectra using artificial neural networks. United States: N. p., 2022.
Web.
Prozuments, Kirills, & Zaleski, Daniel P. Identification and assignment of rotational spectra using artificial neural networks. United States.
Prozuments, Kirills, and Zaleski, Daniel P. Tue .
"Identification and assignment of rotational spectra using artificial neural networks". United States. https://www.osti.gov/servlets/purl/1924874.
@article{osti_1924874,
title = {Identification and assignment of rotational spectra using artificial neural networks},
author = {Prozuments, Kirills and Zaleski, Daniel P.},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {7}
}
Works referenced in this record:
Automated assignment of rotational spectra using artificial neural networks
journal, September 2018
- Zaleski, Daniel P.; Prozument, Kirill
- The Journal of Chemical Physics, Vol. 149, Issue 10
Chemical Compound Discovery Using Machine Learning Technologies
patent-application, September 2019
- Li, Yen; Luo, Heng; Cornell, Wendy Dawn
- US Patent Application 15/920,290; 2019/0286792 Al
Methods and Systems for Identification of DNA Patterns Through Special Analysis
patent-application, May 2009
- Dimitrova, Nevenka; Cheung, Yee Him
- US Patent Application 12/282,435; 2009/0129647 Al
Physical Property Prediction Method and Physical Property Prediction System
patent-application, November 2020
- Suzuki, Kunihiko; Seo, Satoshi; Osaka, Harue
- US Patent Application 16/643,094; 2020/0349451 Al
Artificial intelligence characterizes rotational spectroscopy
journal, September 2018
- Agner, Mary Alexandra
- Scilight, Vol. 2018, Issue 37
Tensor Network Machine Learning System
patent-application, March 2021
- Stojevic, Vid; Shaker, Noor; Bal, Matthias
- US Patent Application 16/618,782; 2021/0081804 Al