Optimal density functional theory to predict electron affinities of polycyclic aromatic hydrocarbon molecules.

[Display omitted] • Evaluated the electron affinity (EA) of polycyclic aromatic hydrocarbons (PAH) molecules using various DFT methods. • Compared computational EA estimates with experimental values from photoelectron spectroscopy. • Found that GGA functionals outperformed hybrid and meta -GGA functionals in estimating EA values for PAH molecules. • Identified the cc-pVTZ basis set as consistently producing satisfactory results across all functionals. • Highlighted the BPBE, CBSB7, and 6-311G(d, p) basis sets as yielding the best EA predictions for PAH molecules. Polycyclic aromatic hydrocarbons (PAH) molecules serve as fundamental building blocks in the formation of graphene, a highly versatile material with diverse applications. Understanding the electrical properties of PAH molecules is pivotal in defining the conductivity of graphene, as the latter's conductive behavior is inherently linked to its molecular structure. Electron affinity (EA) stands out as a crucial parameter in assessing the electrical characteristics of PAH molecules. However, the experimental determination of EA entails significant costs, prompting researchers to turn to computational methods for estimation. Despite advancements in computational resources and theoretical techniques, particularly within density functional theory (DFT), the optimal method for estimating EA remains unclear. In this study, we systematically evaluate various functionals and basis sets to determine the most accurate approach for estimating the electron affinity of PAH molecules. [ABSTRACT FROM AUTHOR]