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NameMr. Yunrui Yan
EmailEmail hidden; Javascript is required.
OrganizationUniversity of Florida
PositionGraduate Student
InvitedNo
TypePoster
TopicComputational Chemistry
Title

Predicting experimentally measured optical gap of conjugated polymers with DFT and machine learning

Author(s)

Bin Liu, Yunrui Yan, Mingjie Liu*

Author Location(s)

University of florida

Abstract

Conjugated polymers (CPs), characterized by their alternating σ and π bonds, have garnered significant attention for their diverse structures and adjustable electronic properties. However, achieving optimal electronic properties, particularly the optical gap (E_opt^exp ) of CPs with high efficiency and accuracy remains challenging due to the expansive chemical space. This study introduces a rational model that combines density functional theory (DFT) calculations and data-driven machine learning (ML) approach to predict the experimentally measured E_opt^exp of CPs. We demonstrated that by removing alkyl side chains and extending the conjugated backbone, the obtained oligomer structure can effectively capture the electronic properties of a CP, significantly improving the correlation between the DFT-calculated HOMO-LUMO gap (E_gap^DFT) and E_opt^exp (R2=0.51). In contrast, E_gap^DFT calculated from the monomer units of CPs showed a much lower correlation (R2=0.15). To further improve the prediction accuracy of E_opt^exp, we trained various conventional ML models using E_gap^DFT from oligomers and multiple molecular representations obtained from monomer units. The HBGR model emerged as the most effective, achieving an R² of 0.755 and an RMSE of 0.1 eV in predicting E_opt^exp. Remarkably, when applied to a new, unseen CP dataset, our model exhibited superior transferability compared to baseline models that relied solely on molecular representations. For the first time, we demonstrated a novel and effective strategy for predicting the experimentally measured fundamental properties of CPs, paving the way for the accelerated design and development of high-performance CPs in photoelectronic applications.

Date05/30/2024