Geometrical features can predict electronic properties of graphene nanoflakes

The experimental discovery of graphene has produced an avalanche of theoretical and computational studies to understand the behaviour of this fascinating material. However, the intrinsic relationships between nanoscale features and graphene stability, electronic properties and reactivity remains poorly investigated. In this work, we correlate the electronic properties of 622 computationally optimized graphene structures to their structural features using machine learning algorithms. Machine learning models of the electron affinity (EA), energy of the Fermi level (EF), electronic band gap (EG) and ionization potential (EI) are calibrated with structural features of 70% of the dataset describing more than 70% of cross-validation variance. Moreover, the predictions of the values of all the properties of a test set of the remaining 30% of dataset were specially accurate with a strong correlation of R2 ∼ 0.9. Machine learning models have tremendous potential to rapidly identify hypothetical nanostructures with desired electronic properties that, considering the latest advances in graphene synthesis and functionalization, could be experimentally prepared in a near future.