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Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning

Abstract

Combining researcher domain expertise and advanced dimension reduction methods we demonstrate how visually comparing the distribution of nanoparticles mapped from multiple dimensions to a two dimensional plane can rapidly identify possible single-structure/property relationships, and to a lesser extent multi-structure/property relationships. These relationships can be further investigated and confirmed with machine learning, using genetic programming to inform the choice of property-specific models and their hyper-parameters. In the case of our nanodiamond case study, we visually identify and confirm a strong relationship between the size and the probability of observation (stability), and a more complicated (and visually ambiguous) relationship between the ionisation potential and band gaps with a range of different structural, chemical and statistical surface features, making it more difficult to engineering in practice.

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Publication details

The article was received on 09 May 2019, accepted on 30 Oct 2019 and first published on 31 Oct 2019


Article type: Paper
DOI: 10.1039/C9NR03940F
Nanoscale, 2019, Accepted Manuscript

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    Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning

    A. S. Barnard and G. Opletal, Nanoscale, 2019, Accepted Manuscript , DOI: 10.1039/C9NR03940F

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