ACCELERATING THE DISCOVERY OF CONDUCTING POLYMER-GRAPHENE NANOCOMPOSITES VIA HIGH-PERFORMANCE COMPUTING AND HIGH-THROUGHPUT SCREENING

Date

2025-4-15

Author

Hacıefendioğlu Aydın, Tuğba

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This thesis presents a comprehensive computational investigation of conducting polymer-graphene/graphene oxide (CP-Gr/GrO) nanocomposites by integrating density functional theory (DFT), molecular dynamics (MD) and machine learning (ML) approaches. Two systematic DFT-based screening studies were carried out on donor-acceptor (D-A) units to evaluate how planarity, conjugation and electron delocalization influence the band gap and charge transport properties. These studies present a systematic structure-property mapping and help identify promising candidates for organic electronic applications. To overcome the limitations of high computational cost in DFT, ML techniques were employed to predict the band gap and hole reorganization energy with high accuracy. A custom dataset comprising more than 3000 D-A type CPs was developed for this purpose, enabling efficient high-throughput screening via descriptor-rich, data-driven models. Furthermore, high-accuracy DFT calculations were used to determine key structural properties that govern polymer alignment on Gr. A custom Python algorithm (Py_alignment_01) was developed to replicate DFT-based alignment predictions, significantly reducing computational cost. The interfacial behavior and self-alignment of CPs on Gr and GrO surfaces were further explored through MD simulations, highlighting the influence of surface oxidation, functional group type and π-π stacking in directing polymer self-organization. Therefore, this work offers insights into the structure-function relationships of CPs and CP-Gr/GrO systems and proposes a comprehensive multiscale modeling framework to accelerate the rational design of high-performance polymer nanocomposites for flexible electronics, energy storage and optoelectronic devices.

Subject Keywords

Graphene/Graphene Oxide, Density Functional Theory, Molecular Dynamics Simulations, Machine Learning, Conducting Polymer

URI

https://hdl.handle.net/11511/114637

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Graduate School of Natural and Applied Sciences, Thesis