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

Published online by Cambridge University Press:  24 April 2019

Amanda S. Barnard Open the ORCID record for Amanda S. Barnard [Opens in a new window] ,
Benyamin Motevalli  and
Baichuan Sun
Amanda S. Barnard*
Affiliation:
CSIRO Data61, Door 34 Village Street, Docklands, VIC 3008, Australia
Benyamin Motevalli
Affiliation:
CSIRO Data61, Door 34 Village Street, Docklands, VIC 3008, Australia
Baichuan Sun
Affiliation:
CSIRO Data61, Door 34 Village Street, Docklands, VIC 3008, Australia
*
Address all correspondence to Amanda S. Barnard at [email protected]
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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.

Type
Research Letters
Information
MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 730 - 736
Copyright
Copyright © Materials Research Society 2019 

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