Advanced simulations of cross-linked thermoset and conjugated polymers for multifunctional applications

Polymers with diverse mechanical, thermal, and optoelectronic properties are essential for applications in flexible electronics, structural materials, and organic semiconductors. However, understanding the structure–property relationships of these materials remain challenging due to complex molecular interactions and hierarchical structure. This dissertation presents a unified multiscale modeling framework that integrates coarse-grained molecular dynamics (CG-MD), density functional theory (DFT), and machine learning (ML) to investigate and optimize cross-linked thermosets and conjugated polymers (CPs). The first part of this study utilizes CG-MD simulations to explore how cross-link density (c) and additive content (m) affect the mechanical and thermal properties of thermoset polymers. Simulations reveal that both parameters significantly influence the polymer network, affecting stiffness, elasticity, and glass transition temperature (Tg). Specifically, increasing cross-link density raises both Tg and fragility, while increasing additive content reduces them. Polymer chain length also plays a crucial role: shorter chains promote phase separation due to reduced entanglement, while longer chains improve network connectivity and mechanical reinforcement. Strain-induced deformation simulations highlight the interplay between segmental dynamics and network structure, offering molecular-scale insights into the tunability of mechanical performance in cross-linked systems. The second part of this dissertation investigates the optoelectronic and photoluminescence (PL) properties of conjugated polymers through hybrid functional DFT calculations. Utilizing a simplified model of cis-polyacetylene (cis-PA) oligomers, ab initio electronic structure methods and time-dependent density matrix formalism are used to examine phonon-induced relaxation dynamics in the photoexcited state. The analysis reveals that, in undoped systems, electrons relax faster than holes, with light emission governed by both inter-band and intra-band transitions. The conductivity of cis-PA is further explored by introducing p-type dopants and injecting charge into pristine material. These modifications significantly alter the electronic structure and enhance conductivity. Detailed analyses demonstrate how dopants and injected charges interact with the polymer matrix, modifying its electronic properties and enabling pathways for improved charge mobility. Additionally, nonadiabatic couplings (NACs) and dissipative excited-state dynamics are computed to assess the role of inter-oligomer interactions. These interactions accelerate relaxation compared to isolated oligomers, leading to broader spectral line widths, redshifted transition energies, and lower PL intensities. To deepen understanding of PL behavior, the effects of doping and functionalization on radiative relaxation dynamics are examined by comparing undoped, doped, and functionalized cis-PA polymer. In undoped ensembles, electron relaxation is faster than hole relaxation. In contrast, doped (phosphorus fluoride) and functionalized systems exhibit a reversal in this trend, with holes relaxing more rapidly. These structural modifications introduce new interband transitions, increase nonradiative recombination rates, and redshift transition energies—ultimately reducing PL quantum yield (PLQY) in doped systems relative to pristine and functionalized counterparts. Collectively, these findings provide a mechanistic understanding of excited-state dynamics, charge transport, and optical behavior in conjugated polymers, offering design insights for flexible electronics and organic optoelectronic materials. The final part of this dissertation presents a ML-based Quantitative Structure Property Relationship (QSPR) approach to predict Tg for a database of 250 polymer systems. Various ML models were compared, with the Multi-Layer Perceptron (MLP) showing superior predictive performance over other linear and non-linear models. The analysis also identified key molecular descriptors related to Tg, offering computational guidelines for optimizing polymer thermal stability and mechanical performance. Since ML was defined earlier, it is used freely here as advised. This strategy offers a computationally efficient means of designing polymers with enhanced temperature resilience and mechanical tunability. By integrating molecular simulations, quantum chemistry, and data-driven modeling, this dissertation establishes a unified framework for predicting and optimizing the physical properties of advanced polymers. The insights gained contribute to the rational development of cross-linked thermosets and conjugated polymers, ultimately supporting innovation in flexible electronics, high-performance polymer composites, and organic optoelectronic device.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

dissertation

Discipline thesis:degree_discipline

Engineering

Department dc:contributor.department

Department of Aerospace Engineering

Grantor

Iowa State University

Year dc:date.issued

2025

Author dc:creator

• Keya, Kamrun Nahar

Advisors dc:contributor.advisor

• Xia, Wenjie
• Sheidaei, Azadeh
• He, Ping
• Runnels, Brandon
• Ajmera, Beena

Language dc:language.iso

en

DOI

https://doi.org/10.31274/td-20251215-47

OAI identifier oai:identifier

oai:dr.lib.iastate.edu:20.500.12876/dvmq6bkv