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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A post-publication change was made to this article on 19 Mar 2019 to correct the copyright line.