Unsupervised data mining in nanoscale x-ray spectro-microscopic study of NdFeB magnet
Abstract
Novel developments in X-ray based spectro-microscopic characterization techniques have increased the rate of acquisition of spatially resolved spectroscopic data by several orders of magnitude over what was possible a few years ago. This accelerated data acquisition, with high spatial resolution at nanoscale and sensitivity to subtle differences in chemistry and atomic structure, provides a unique opportunity to investigate hierarchically complex and structurally heterogeneous systems found in functional devices and materials systems. However, handling and analyzing the large volume data generated poses significant challenges. Here we apply an unsupervised data-mining algorithm known as DBSCAN to study a rare-earth element based permanent magnet material, Nd2Fe14B. We are able to reduce a large spectro-microscopic dataset of over 300,000 spectra to 3, preserving much of the underlying information. Scientists can easily and quickly analyze in detail three characteristic spectra. Our approach can rapidly provide a concise representation of a large and complex dataset to materials scientists and chemists. For instance, it shows that the surface of common Nd2Fe14B magnet is chemically and structurally very different from the bulk, suggesting a possible surface alteration effect possibly due to the corrosion, which could affect the material’s overall properties.
- Authors:
-
- Wuhan Univ., Hubei (China)
- UCSF, San Francisco, CA (United States)
- Stanford Univ., Stanford, CA (United States)
- Center for High Pressure Science and Technology Advanced Research, Shanghai (China)
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Publication Date:
- Research Org.:
- SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1331596
- Report Number(s):
- SLAC-PUB-16817
Journal ID: ISSN 2045-2322; srep34406; TRN: US1601863
- Grant/Contract Number:
- AC02-76SF00515
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 6; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 36 MATERIALS SCIENCE
Citation Formats
Duan, Xiaoyue, Yang, Feifei, Antono, Erin, Yang, Wenge, Pianetta, Piero, Ermon, Stefano, Mehta, Apurva, and Liu, Yijin. Unsupervised data mining in nanoscale x-ray spectro-microscopic study of NdFeB magnet. United States: N. p., 2016.
Web. doi:10.1038/srep34406.
Duan, Xiaoyue, Yang, Feifei, Antono, Erin, Yang, Wenge, Pianetta, Piero, Ermon, Stefano, Mehta, Apurva, & Liu, Yijin. Unsupervised data mining in nanoscale x-ray spectro-microscopic study of NdFeB magnet. United States. https://doi.org/10.1038/srep34406
Duan, Xiaoyue, Yang, Feifei, Antono, Erin, Yang, Wenge, Pianetta, Piero, Ermon, Stefano, Mehta, Apurva, and Liu, Yijin. Thu .
"Unsupervised data mining in nanoscale x-ray spectro-microscopic study of NdFeB magnet". United States. https://doi.org/10.1038/srep34406. https://www.osti.gov/servlets/purl/1331596.
@article{osti_1331596,
title = {Unsupervised data mining in nanoscale x-ray spectro-microscopic study of NdFeB magnet},
author = {Duan, Xiaoyue and Yang, Feifei and Antono, Erin and Yang, Wenge and Pianetta, Piero and Ermon, Stefano and Mehta, Apurva and Liu, Yijin},
abstractNote = {Novel developments in X-ray based spectro-microscopic characterization techniques have increased the rate of acquisition of spatially resolved spectroscopic data by several orders of magnitude over what was possible a few years ago. This accelerated data acquisition, with high spatial resolution at nanoscale and sensitivity to subtle differences in chemistry and atomic structure, provides a unique opportunity to investigate hierarchically complex and structurally heterogeneous systems found in functional devices and materials systems. However, handling and analyzing the large volume data generated poses significant challenges. Here we apply an unsupervised data-mining algorithm known as DBSCAN to study a rare-earth element based permanent magnet material, Nd2Fe14B. We are able to reduce a large spectro-microscopic dataset of over 300,000 spectra to 3, preserving much of the underlying information. Scientists can easily and quickly analyze in detail three characteristic spectra. Our approach can rapidly provide a concise representation of a large and complex dataset to materials scientists and chemists. For instance, it shows that the surface of common Nd2Fe14B magnet is chemically and structurally very different from the bulk, suggesting a possible surface alteration effect possibly due to the corrosion, which could affect the material’s overall properties.},
doi = {10.1038/srep34406},
journal = {Scientific Reports},
number = ,
volume = 6,
place = {United States},
year = {Thu Sep 29 00:00:00 EDT 2016},
month = {Thu Sep 29 00:00:00 EDT 2016}
}
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