Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

Machine learning: Spying enhanced materials with x-ray vision Using algorithms to automatically spot variations in massive X-ray diffraction data sets may improve design of multi-component alloys. Having three or more metals in an alloy can lead to overwhelming combinations of possible materials, ea...

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Autores principales: Yuma Iwasaki, A. Gilad Kusne, Ichiro Takeuchi
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2017
Materias:
Materials of engineering and construction. Mechanics of materials
TA401-492
Computer software
QA76.75-76.765
Acceso en línea:https://doaj.org/article/64f79ca4c771418085a08eb761244bfb
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spelling oai:doaj.org-article:64f79ca4c771418085a08eb761244bfb2021-12-02T16:19:59ZComparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries10.1038/s41524-017-0006-22057-3960https://doaj.org/article/64f79ca4c771418085a08eb761244bfb2017-02-01T00:00:00Zhttps://doi.org/10.1038/s41524-017-0006-2https://doaj.org/toc/2057-3960Machine learning: Spying enhanced materials with x-ray vision Using algorithms to automatically spot variations in massive X-ray diffraction data sets may improve design of multi-component alloys. Having three or more metals in an alloy can lead to overwhelming combinations of possible materials, each with different properties. A. Gilad Kusne from the National Institute of Standards and co-workers examined how machine learning techniques could simplify alloy discovery through ‘dissimilarity measures’ that quantify how key structural data points, such as the positions and intensities of X-ray peaks, change with sample makeup. The team fabricated a compositional spread of iron–cobalt–nickel thin film alloys, and then evaluated different software approaches to finding X-ray dissimilarities for both processing speed and accuracy. Several algorithms suitable for high-throughput generation of color-coded maps that display relations between alloy composition and phase in both two and three-dimensions were identified.Yuma IwasakiA. Gilad KusneIchiro TakeuchiNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 3, Iss 1, Pp 1-9 (2017)
institution DOAJ
collection DOAJ
language EN
topic Materials of engineering and construction. Mechanics of materials
TA401-492
Computer software
QA76.75-76.765
spellingShingle Materials of engineering and construction. Mechanics of materials
TA401-492
Computer software
QA76.75-76.765
Yuma Iwasaki
A. Gilad Kusne
Ichiro Takeuchi
Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries
description Machine learning: Spying enhanced materials with x-ray vision Using algorithms to automatically spot variations in massive X-ray diffraction data sets may improve design of multi-component alloys. Having three or more metals in an alloy can lead to overwhelming combinations of possible materials, each with different properties. A. Gilad Kusne from the National Institute of Standards and co-workers examined how machine learning techniques could simplify alloy discovery through ‘dissimilarity measures’ that quantify how key structural data points, such as the positions and intensities of X-ray peaks, change with sample makeup. The team fabricated a compositional spread of iron–cobalt–nickel thin film alloys, and then evaluated different software approaches to finding X-ray dissimilarities for both processing speed and accuracy. Several algorithms suitable for high-throughput generation of color-coded maps that display relations between alloy composition and phase in both two and three-dimensions were identified.
format article
author Yuma Iwasaki
A. Gilad Kusne
Ichiro Takeuchi
author_facet Yuma Iwasaki
A. Gilad Kusne
Ichiro Takeuchi
author_sort Yuma Iwasaki
title Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries
title_short Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries
title_full Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries
title_fullStr Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries
title_full_unstemmed Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries
title_sort comparison of dissimilarity measures for cluster analysis of x-ray diffraction data from combinatorial libraries
publisher Nature Portfolio
publishDate 2017
url https://doaj.org/article/64f79ca4c771418085a08eb761244bfb
work_keys_str_mv AT yumaiwasaki comparisonofdissimilaritymeasuresforclusteranalysisofxraydiffractiondatafromcombinatoriallibraries
AT agiladkusne comparisonofdissimilaritymeasuresforclusteranalysisofxraydiffractiondatafromcombinatoriallibraries
AT ichirotakeuchi comparisonofdissimilaritymeasuresforclusteranalysisofxraydiffractiondatafromcombinatoriallibraries
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