Harnessing DFT and machine learning for accurate optical gap prediction in conjugated polymers
Bin Liu, Yunrui Yan, Mingjie Liu
Abstract
expgap, falling within the experimental error margin of ∼0.1 eV. We further validated XGBoost-2 on a dataset of 227 newly synthesized CPs collected from literature without further retraining. Notably, XGBoost-2 exhibits both excellent interpolation for BT-, BTA-, QA-, DPP-, and TPD-based CPs, and exceptional extrapolation for PDI-, NDI-, DTBT-, BBX-, and Y6-based CPs, which are attributed to the integration of DFT methods with rationally designed oligomer structures. For the first time, we demonstrated a novel and effective strategy combining quantum chemistry calculations with ML modeling for accurate and efficient prediction of experimentally measured fundamental properties of CPs. Our study paves the way for the accelerated design and development of high-performance CPs in photoelectronic applications.