Predicting archetypal nanoparticle shapes using a combination of thermodynamic theory and machine learning

Yan, T, Sun, B and Barnard, A 2018, 'Predicting archetypal nanoparticle shapes using a combination of thermodynamic theory and machine learning', Nanoscale, vol. 10, no. 46, pp. 21818-21826.


Document type: Journal Article
Collection: Journal Articles

Title Predicting archetypal nanoparticle shapes using a combination of thermodynamic theory and machine learning
Author(s) Yan, T
Sun, B
Barnard, A
Year 2018
Journal name Nanoscale
Volume number 10
Issue number 46
Start page 21818
End page 21826
Total pages 9
Publisher Royal Society of Chemistry
Abstract Machine learning is a useful way of identifying representative or pure nanoparticle shapes as part of a larger ensemble, but its predictive capabilities can be limited when a large dataset of candidate structures must already exist. Ideally one would like to use machine learning to define the ideal dataset for future, more computationally intensive, studies before a significant amount of resources are consumed. In this work we combine an established analytical phenomenological model and statistical machine learning to predict the archetypes and prototypes of a diverse ensemble of 2380 platinum nanoparticle morphologies developed with less than twenty input electronic structure simulations. By parameterising a size- and shape-dependent thermodynamic model, probabilities are assigned to seventeen different shapes between three and thirty nanometres, which together with structural features such as nanoparticle diameter, surface area, sphericity and facet configuration form the basis for archetypal analysis and K-means clustering. Using this approach we rapidly identify six "pure" archetypes and twelve "representative" prototypes that can be used in future computational studies of properties such as catalysis.
Subject Physical Sciences not elsewhere classified
DOI - identifier 10.1039/c8nr07341d
Copyright notice © 2018 The Royal Society of Chemistry
ISSN 2040-3364
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