Big‐Data Analysis of Geometric Descriptors as Efficient Predictors of Energetic Stability in Nonplanar Polycyclic Aromatic Hydrocarbons

ABSTRACT

Accurate, efficient stability predictors are essential for understanding isomer formation in polycyclic aromatic hydrocarbons (PAHs), with implications for pollution toxicity and carbon‐material design, holding broad environmental and technological significance. Recently, a benchmark study demonstrated that PBE0‐D4 reproduces CCSD(T)‐level isomerization energies for 335 PAHs with a mean absolute deviation (MAD) of 0.67 kcal mol⁻¹. Here, we apply the PBE0‐D4/6‐31G(2df,p) level of theory to 38,264 PAH isomers from the COMPAS‐3x database and identify fast, geometry‐based parameters that predict isomer stability. The total dihedral deviation (Σ_Dihedral) provides a cost‐free nonplanarity metric yielding a mean absolute deviation (MAD) of 3.6 kcal mol⁻¹, outperforming maximal z‐displacement (MAD = 4.8 kcal mol⁻¹) and the Harmonic Oscillator Model of Aromaticity (HOMA; MAD = 5.3 kcal mol⁻¹). A combined Σ_Dihedral–HOMA model reduces the MAD to 2.5 kcal mol⁻¹, and adding a fitted semiempirical xTB correction further lowers the MAD to 0.8 kcal mol⁻¹. We implement these descriptors in the PAH Automated Property Scanner (PAHAPS) web tool, enabling rapid estimation of PAH isomer energies from molecular coordinates without intensive quantum calculations. This integrated approach facilitates large‐scale screening and efficient design of stable PAH isomers for environmental and materials applications.