Quantitative Structure–Property Relationship Modeling of Electronic Properties of Graphene Using Atomic Radial Distribution Function Scores

The intrinsic relationships between nanoscale features and electronic properties of nanomaterials remain poorly investigated. In this work, electronic properties of 622 computationally optimized graphene structures were mapped to their structures using partial-least-squares regression and radial distributions function (RDF) scores. Quantitative structure–property relationship (QSPR) models were calibrated with 70% of a virtual data set of 622 passivated and nonpassivated graphenes, and we predicted the properties of the remaining 30% of the structures. The analysis of the optimum QSPR models revealed that the most relevant RDF scores appear at interatomic distances in the range of 2.0 to 10.0 Å for the energy of the Fermi level and the electron affinity, while the electronic band gap and the ionization potential correlate to RDF scores in a wider range from 3.0 to 30.0 Å. The predictions were more accurate for the energy of the Fermi level and the ionization potential, with more than 83% of explained data variance, while the electron affinity exhibits a value of ∼80% and the energy of the band gap a lower 70%. QSPR models have tremendous potential to rapidly identify hypothetical nanomaterials with desired electronic properties that could be experimentally prepared in the near future.