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: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2017
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/64f79ca4c771418085a08eb761244bfb |
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| Sumario: | 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. |
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