In this study, the degree of the π-orbital overlap (DPO) model proposed earlier for polycyclic aromatic hydrocarbons (PAH) was employed to develop quantitative structure–property relationships (QSPRs) for band gaps, ionization potentials, and electron affinities of thienoacenes. DPO is based on two-dimensional topological draw of aromatic molecules. The B3LYP/6-31+G(d) level of density functional theory (DFT) was used to provide chemical data for developing QSPRs. We found that the DPO model is able to capture the correct physics of electronic properties of aromatic molecules so that with only six nonzero topological parameters (four for PAH and additional two for thienoacenes), the DPO model yields the linear dependence of electronic properties of both the PAH and thienoacenes classes by a single set of QSPRs with the accuracy to within 0.1 eV of the DFT results. The results suggest that within the DPO framework, all aromatic molecules can share the same set of QSPRs.

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The degree of π orbital overlap (DPO) model has been demonstrated to be an excellent quantitative structure–property relationship (QSPR) that can map two-dimensional structural information of polycyclic aromatic hydrocarbons (PAHs) and thienoacenes to their electronic properties, namely, band gaps, electron affinities, and ionization potentials. However, the model suffers from significant limitations that narrow its applications due to inefficient manual procedures in parameter optimization and descriptor formulation. In this work, we developed a machine learning (ML)-based method for efficiently optimizing DPO parameters and proposed a truncated DPO descriptor, which is simple enough that can be automatically extracted from simplified molecular-input line-entry system strings of PAHs and thienoacenes. Compared with the result from our previous studies, the ML-based methodology can optimize DPO parameters with four times fewer data, while it can achieve the same level of accuracy in predictions of the mentioned electronic properties to within 0.1 eV. The truncated DPO model also has similar accuracy to the full DPO model. Consequently, the ML-based DPO approach coupled with the truncated DPO model enables new possibilities for developing automatic pipelines for high-throughput screening and investigating new QSPR for new chemical classes.

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