Archetype and Prototype Analysis of Graphene-Oxide Nanoflakes

Dr Benyamin Motevalli1, Dr Amanda Barnard1, Dr Baichuan Sun1

1CSIRO Data61, Docklands, Australia


Since its discovery in 2004, graphene has attracted massive attention in a range of industries. Graphene is the lightest, strongest, most electrically conductive substance on earth. However, after over a decade of intense research and development graphene products are still almost non-existent. In this respect, computational material science community have also devoted a substantial effort, specially, in establishing the structure-property relationship of graphene oxides (GOs). To date, most available studies are limited to few cases of specific topologies, sizes, and oxygen contents.

Here, using intensive electronic structure calculations, more than 60,000 virtual experiments is conducted to develop a large dataset of GO nanoflakes. To characterise GOs, a range of post-processing techniques were employed, which yield more than 500 descriptors, from which 90 features were identified to effectively describe the dataset. Then, clustering and archetypal analysis is used to investigate the GO dataset and identify the most significant structures. It is observed that at least 20 archetypes are required to explain more than 60% variance, while 45 prototypes should be included to get a similar value. Since the archetypes are the pure types of the convex hull, their combination can more efficiently describe a larger number of members of the ensemble in comparison with the prototypes. To visualise the 90-dimensional dataset, all structures are mapped to the convex hull of the archetypes and are coloured by different outputs as well as clusters. The profiles of the archetypes and prototypes are also extracted and compared with their closest match in the dataset.


Dr. Benyamin Motevalli is a Postdoctoral Fellow in Data61 at CSIRO. He has years of experience in developing/employing computational and numerical analysis techniques to establish fundamental understanding of novel intelligent nanomaterials.  His current research focuses on rational design of materials through innovative data-driven models that offer the advantage of fusing complex experimental and computational data for a higher-level understanding of structure-processing-property relationships


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