Detour Ring Index as a Molecular Descriptor for Predicting Gas Chromatographic Retention of Sulfur Polycyclic Aromatics

Exploration and Screening of Database of Polycyclic Aromatic Hydrocarbons to Find Compounds with Lower Enthalpy of Formation

Coulomb 2D-Snapshot Model for Polycyclic Aromatic Hydrocarbons: A Simple Predictor of Electronic Properties

We extend the Coulomb Snapshot model from one-dimensional conjugated chains to two-dimensional polycyclic aromatic hydrocarbons (PAHs). The original 1D formulation, which quantifies π-electron delocalization through electron–nuclear Coulombic interactions, showed strong logarithmic correlations (R2 = 0.98) with experimental absorption wavelengths and combustion enthalpies in linear oligoenes and oligoynes. The 2D extension introduces geometry-specific interaction factors for rectangular, linear, and hexagonal (regular and irregular) PAH topologies. Statistical validation on 20 structurally diverse PAHs (C12–C54) yields R2 = 0.656 for ionization energy (MAE = 0.221 eV) and R2 = 0.672 for the fundamental gap (MAE = 0.441 eV) across the complete dataset; after z-score outlier removal (|z| > 1.5σ), these improve to mean relative errors of 2.25% for ionization energy (MAE = 0.154 eV, R2 = 0.857) and 5.29% for the fundamental gap (MAE = 0.298 eV, R2 = 0.843). Three outliers (acenaphthylene, fluoranthene, dibenzo[bc,kl]coronene) show systematic deviations attributable to five-membered-ring fusion and extended irregular hexagonal geometries not fully captured by the simplified geometric factors. Error analysis—normally distributed residuals, a narrow 95% prediction band (±0.38 eV for IE, ±0.75 eV for the fundamental gap), and consistent performance across molecular sizes—supports predictive reliability while maintaining physical interpretability and minimal computational cost. Although developed and evaluated on a relatively small dataset, the 2D-Snapshot model thus offers a fast, physically interpretable estimate of the electronic properties of benzenoid PAHs and represents a promising framework for rapid screening in organic materials design that warrants validation on larger, more structurally diverse sets.

Can the outcome of an aromatic edge migration be predicted on-the-fly?

Kinetic Monte Carlo (kMC) simulations of polycyclic aromatic hydrocarbon (PAH) growth require on-the-fly evaluation of equilibrium constants (Keq) for aromatic edge migration reactions. Here we investigate whether Keq values for five- and seven-membered ring migration reactions can be predicted from molecular structural descriptors with sufficient accuracy for kMC use. We show that no single structural index correlates adequately with Keq, but that a combination of complementary descriptors-including HOMA-based aromaticity indices, graph-theoretical measures, and C-atom ring environment contributions-yields significantly more accurate correlations. We further introduce parametrization based on BOIA, a bond-order-based analogue of HOMA, and demonstrate that supplementing PM7/MOPAC energies with BOIA-derived structural descriptors and a group-additivity scheme yields a correlation coefficient of 0.95, with the test set quality closely matching that of the training set. These results suggest that the PM7/MOPAC approach is a promising candidate for on-the-fly Keq prediction in kMC simulations of PAH growth.

Quantitative Analysis of Status-Based Topological Indices for Predictive Modeling of Molecular Properties

Polycyclic aromatic hydrocarbons (PAHs) are a class of aromatic molecules consisting of multiple fused benzene rings. In chemical graph theory, topological indices play a crucial role in exploring structure-property relationships and in forecasting physicochemical behavior and biological responses of PAH compounds. Such indices are extensively utilized in computational chemistry, drug discovery, and quantitative structure-property relationship (QSPR) modeling. In this work, we investigate the predictive capability of several status-based topological indices, namely the First Status Connectivity index S_1 (G), Second Status Connectivity index S_2 (G), Nirmala Status index SN(G), Forgotten Status index SF(G), Status Sombor index SSO(G), and Status Elliptic Sombor index SESO(G) for a selected set of 38 high-priority PAHs. Among the studied descriptors, the Nirmala Status index exhibits the highest predictive accuracy, particularly for molar refractivity and polarizability (R ≈ 0.979). Furthermore, the variation in the best-performing index across linear, quadratic, and cubic regression models is illustrated in comparative plots based on minimum RMSE values, providing greater clarity and interpretation. The results demonstrate that status-based indices effectively capture long-range structural and electronic characteristics of PAHs. This study highlights the potential of distance-based topological descriptors as reliable tools in QSPR modeling.

Quantum-Chemical Screening of Designed Heterocyclic Polymer Dimers Combined with Small-Data Regression Modeling for Organic Solar Cell Materials

We report a two-layer computational workflow for designed heterocyclic polymer dimers as candidates for organic solar cell (OSC) materials. The workflow integrates geometry-optimized B3LYP/6-31G(d) quantum-chemical descriptors (HOMO, LUMO, gap, dipole moment) computed for five fully disclosed monomer–dimer pairs (1m–5m; 1d–5d), with a verified, literature-curated 17-entry OSC dataset (PCE 3.6–19.9%, years 2016–2024) modeled by a non-tautological ridge regression baseline (Model A; predictors Year + source_block + log10 hole mobility). All five dimers were computed under uniform neutral closed-shell conditions. Pareto-front analysis in the gap–dipole descriptor space identifies dimer 2d (difluorinated thiophene–diazine D-A dimer; gap 1.74 eV, dipole 16.59 D) as the Tier I lead candidate, with 3d (bis(thiophene–triazine) dimer) and 1d (bis-thiophene–thiazole dimer) as additional Tier I candidates. Model A yields R2(LOOCV) = 0.660, MAE = 2.36%, and RMSE = 3.67%, surviving a 500-shuffle permutation null at empirical p < 0.001. A descriptor-augmented Model B (Eg + HOMO added) demonstrates that the present literature dataset cannot support a deployable molecular-descriptor regression without expansion. The combined DFT–regression workflow provides a transparent screening framework that identifies 2d as the priority synthesis target.

Integrated QSAR, Electrochemical, and Computational Evaluation of Green Synthesized Pyrimidine-Based Inhibitors for Copper Corrosion in 3.5% NaCl