In summary, descriptors are essential for ML models to capture critical information influencing targeted properties and learn structure-property relationships effectively. DFT-calculated HOMO-LUMO gaps of modified oligomers and ECFP6 MF derived from unmodified monomers can effectively capture fundamental characteristics of both the extended backbone and alkyl side chains, enabling accurate and efficient prediction of E exp gap values.