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.