Application of Machine Learning in Developing Quantitative Structure-Property Relationship for Electronic Properties of Polyaromatic Compounds.

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.