System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra
Abstract
A method of supervised machine learning-based spectrum analysis information, using a neural network trained with spectrum information, to identify a specified feature of a given material, a system for supervised machine learning-based spectrum analysis, and a method of training a neural network to analyze spectrum data. The method of supervised machine learning-base spectrum analysis comprises inputting into the neural network spectrum data obtained from a sample of the given material; and the neural network processing the spectrum data, in accordance with the training of the neural network, and outputting one or more values for the specified feature of the sample of the material. In an embodiment, the training set of data includes x-ray absorption spectroscopy data for the given material. In an embodiment, the training set of data includes electron energy loss spectra (EELS) data.
- Inventors:
- Issue Date:
- Research Org.:
- State Univ. of New York (SUNY), Albany, NY (United States)
- Sponsoring Org.:
- USDOE; National Science Foundation (NSF)
- OSTI Identifier:
- 1860141
- Patent Number(s):
- 11193884
- Application Number:
- 16/460,117
- Assignee:
- The Research Foundation for the State University of New York (Albany, NY)
- Patent Classifications (CPCs):
-
G - PHYSICS G01 - MEASURING G01N - INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- DOE Contract Number:
- FG02-03ER15476; DMR-1006232
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 07/02/2019
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Frenkel, Anatoly, and Timosenko, Janis. System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra. United States: N. p., 2021.
Web.
Frenkel, Anatoly, & Timosenko, Janis. System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra. United States.
Frenkel, Anatoly, and Timosenko, Janis. Tue .
"System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra". United States. https://www.osti.gov/servlets/purl/1860141.
@article{osti_1860141,
title = {System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra},
author = {Frenkel, Anatoly and Timosenko, Janis},
abstractNote = {A method of supervised machine learning-based spectrum analysis information, using a neural network trained with spectrum information, to identify a specified feature of a given material, a system for supervised machine learning-based spectrum analysis, and a method of training a neural network to analyze spectrum data. The method of supervised machine learning-base spectrum analysis comprises inputting into the neural network spectrum data obtained from a sample of the given material; and the neural network processing the spectrum data, in accordance with the training of the neural network, and outputting one or more values for the specified feature of the sample of the material. In an embodiment, the training set of data includes x-ray absorption spectroscopy data for the given material. In an embodiment, the training set of data includes electron energy loss spectra (EELS) data.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {12}
}
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