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Feature Engineering for Materials Chemistry - Does Size Matter?

07:00 EST 7th February 2019 | BioPortfolio

Summary of "Feature Engineering for Materials Chemistry - Does Size Matter?"

The effects of structural featurisers in the prediction of band gaps have been investigated through machine learning by application to a silver nanoparticle dataset and 2254 potential light-harvesting materials with known band gap. Elemental properties were extended with structural features, via Voronoi polyhedra which allows for neighbour effects, so presumably giving a better representation of the extended system. However, we did not find any noticeably significant difference to the predictive performance of our model. The biggest improvement to our model was from including band gaps calculated from density functional theory. This resulted in a model which could predict the band gaps of the 2254 light-harvesting dataset with an accuracy reflected in a root-mean-square error of 0.232 eV and mean absolute error of 0.142 eV. Furthermore, the good performance of our model was transferable to the prediction of a set of 72 experimental band gaps, independent of the training set, giving a root-mean-square error of 0.91 eV and mean absolute error of 0.76 eV.

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This article was published in the following journal.

Name: Journal of chemical information and modeling
ISSN: 1549-960X
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