Identification of Nanoparticle Prototypes and Archetypes.

High-throughput (HT) computational characterization of nanomaterials is poised to accelerate novel material breakthroughs. The number of possible nanomaterials is increasing exponentially along with their complexity, and so statistical and information technology will play a fundamental role in rationalizing nanomaterials HT data. We demonstrate that multivariate statistical analysis of heterogeneous ensembles can identify the truly significant nanoparticles and their most relevant properties. Virtual samples of diamond nanoparticles and graphene nanoflakes are characterized using clustering and archetypal analysis, where we find that saturated particles are defined by their geometry, while nonsaturated nanoparticles are defined by their carbon chemistry. At the complex hull of the nanostructure spaces, a combination of complex archetypes can efficiency describe a large number of members of the ensembles, whereas the regular shapes that are typically assumed to be representative can only describe a small set of the most regular morphologies. This approach provides a route toward the characterization of computationally intractable virtual nanomaterial spaces, which can aid nanomaterials discovery in the foreseen big data scenario.