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Texture based image classification for nanoparticle surface characterisation and machine learning

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Published 17 September 2018 © 2018 Commonwealth Scientific and Industrial Research Organisation. Published by IOP Publishing Ltd
, , Citation Baichuan Sun and Amanda S Barnard 2018 J. Phys. Mater. 1 016001 DOI 10.1088/2515-7639/aad9ef

2515-7639/1/1/016001

Abstract

Restricting materials informatics to the numerical parameters output from conventional materials modelling software restricts us to a subset of machine learning methods capable of uncovering structure/property relationships and driving materials discovery and design. Presented here is a simple way of converting materials structures in to unique image-based fingerprints suitable for image processing methods, that does not require subjective pre-assessment of the data and selection of descriptors by the user. This combination of methods is shown to classify the morphologies in a set of 425 silver nanoparticles in a meaningful way, and predict the correlation with the energy of the Fermi level in agreement with other machine learning methods that required user intervention. Moving to an image-based, rather than feature list-based, description of nanoparticles and materials brings us one step closer to using experimental micrographs as inputs for machine learning.

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10.1088/2515-7639/aad9ef