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Identifying hidden high-dimensional structure/property relationships using self-organizing maps

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Abstract

Unlike other data intensive domains, understanding distributions, trends, correlations, and relationships in materials data sets typically involves navigating high-dimensional spaces with only a limited number of observations. Under these conditions extracting structure/property relationships is not straightforward and considerable attention must be given to the reduction of feature space before predictions can be made. Here we have used Kohonen networks (self-organizing maps) to identify hidden structure/property relationships in computational sets of twinned and single-crystal diamond nanoparticles based on structural similarity in multiple dimensions, and confirmed the importance of a limited number of surface chemical features using regression.

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Authors and Affiliations

  1. CSIRO Data61, Door 34 Village Street, Docklands, VIC, 3008, Australia

    Amanda S. Barnard, Benyamin Motevalli & Baichuan Sun

Authors
  1. Amanda S. Barnard
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  2. Benyamin Motevalli
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  3. Baichuan Sun
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Corresponding author

Correspondence to Amanda S. Barnard.

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The supplementary material for this article can be found at https://doi.org/10.1557/mrc.2019.36

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Barnard, A.S., Motevalli, B. & Sun, B. Identifying hidden high-dimensional structure/property relationships using self-organizing maps. MRS Communications 9, 730–736 (2019). https://doi.org/10.1557/mrc.2019.36

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  • DOI: https://doi.org/10.1557/mrc.2019.36

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