Neural Network Analysis of X-ray Absorption Spectra for Nanoparticle Structure
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Solution Overview
Problem
Current methods for analyzing X-ray absorption spectroscopy (XAS) data struggle to accurately determine the 3D structure of materials, especially disordered nanomaterials and those under extreme conditions, due to limitations in experimental techniques and analysis methods available for commercial instruments.
Innovation Solution
The use of supervised machine learning, specifically artificial neural networks, is employed to analyze XAS data, including XANES and EXAFS, to extract structural information by training the networks with theoretical simulations and experimental data, allowing for the determination of nanoparticle size, shape, and coordination numbers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional XAS analysis methods are used, then the analysis can be performed with standard experimental techniques, but the accuracy of determining 3D structure of disordered nanomaterials is insufficient
Solution Approach 1:
The patent introduces supervised machine learning models as an intermediary between raw XAS spectral data and structural parameters. The ML models are trained on theoretical simulations to learn the complex mapping from spectra to 3D structural information, enabling accurate determination of coordination numbers and local structure in disordered nanomaterials without requiring complex conventional analysis procedures
Solution Approach 2:
The patent replaces traditional mechanical/iterative fitting methods with machine learning-based analysis. Instead of using complex iterative refinement algorithms and theoretical model fitting, the system uses trained neural networks or other ML models to directly predict structural parameters from spectral data, significantly simplifying the analysis process while improving accuracy for disordered systems
2Measurement precision
If advanced analysis methods are developed to improve structural determination accuracy, then the sensitivity can be extended to fourth coordination shell, but the ease of operation in laboratory settings is reduced
Solution Approach 1:
The patent performs preliminary action by training machine learning models on extensive theoretical simulations before actual data analysis. The models are pre-trained to recognize spectral features corresponding to different coordination shells and structural parameters, enabling them to accurately analyze experimental data from laboratory settings without requiring complex post-processing or specialized expertise
Solution Approach 2:
The patent uses theoretical simulations as copies or proxies for actual experimental data during the training phase. By training on synthesized spectral data with known structural parameters, the ML models learn to extract structural information from real experimental spectra, making the advanced analysis method as easy to operate as conventional methods while achieving extended sensitivity to fourth coordination shell
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the precise determination of nanoparticle structures, extending the sensitivity of XAS techniques to the fourth coordination shell, and allows for analysis in laboratory settings, high temperatures, and complex environments, overcoming limitations of existing methods.
Implementation Method 1
X-ray absorption spectroscopy (XAS) is a widely used technique to determine local atomic structure and/or electronic structure of matter. X-ray absorption spectroscopy data are obtained by measuring transmission and/or yield of fluorescent x-rays or secondary electrons of an element in a material as a function of incident x-ray energy
Data Source
AI summary
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.