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

Dr Chris Feigl1, Dr Benjamin Motevalli1, Dr Baichun Sun1, Dr Amanda Parker1, Dr Amanda Barnard1

1CSIRO, Docklands, Australia

 

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 forrest regressors with extra trees we find that these classes are and either size-dependent or anisotropy-dependent, respectively. This suggests that the purification or screening of nanodiamond samples to remove strained, heterogeneous or anisotropic particles may be undertaken based on the negative electron affinity.


Biography:

Dr Chris Feigl is a Research Scientist working within the Materials and Molecular Modelling team of Data61, CSIRO.  He completed his PhD in Theoretical Condensed Matter Physics from RMIT University in 2012, after which he went into executive management for education and training and humanitarian aid organisations in the middle-east region.  Since returning to Australia, Chris’s research has re-focused on applying machine learning methods to the prediction and characterisation of nanomaterial properties.

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