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Issue 4, 2019
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Classifying and predicting the electron affinity of diamond nanoparticles using machine learning

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Abstract

Using a combination of electronic structure simulations and machine learning we have shown that the characteristic negative electron affinity (NEA) of hydrogenated diamond nanoparticles exhibits a class-dependent structure/property relationship. Using a random forest classifier we find that the NEA will either be consistent with bulk diamond surfaces, or much higher than the bulk diamond value; and using class-specific random forest regressors with extra trees we find that these classes are either size-dependent or anisotropy-dependent, respectively. This suggests that the purification or screening of nanodiamond samples to improve homogeneity may be undertaken based on the negative electron affinity.

Graphical abstract: Classifying and predicting the electron affinity of diamond nanoparticles using machine learning

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Supplementary files

Article information


Submitted
31 Jan 2019
Accepted
15 Mar 2019
First published
20 Mar 2019

Nanoscale Horiz., 2019,4, 983-990
Article type
Communication

Classifying and predicting the electron affinity of diamond nanoparticles using machine learning

C. A. Feigl, B. Motevalli, A. J. Parker, B. Sun and A. S. Barnard, Nanoscale Horiz., 2019, 4, 983
DOI: 10.1039/C9NH00060G

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