Development of Quantum Chemistry based Workflows for the Theoretical Description of Organic Electronics

Abstract

Organic electronics (OE) have become increasingly significant in daily life. Groundbreaking inventions such as organic solar cells (OSCs), organic light-emitting diodes (OLEDs), and organic field-effect transistors (OFETs) have revolutionized the field of sustainable materials for energy conversion. OE enable the fabrication of molecular circuits with integrated functionalities like sensors and detectors, useful in fields such as molecular computing, nanomedicine, and the Internet of Things (IoT). Compared to conventional inorganic electronics, OE offer advantages regarding their sustainability and reduced production cost due to the use of organic or even plant-based materials, as well as versatility and tunable properties for customized solutions like printable screens, curved photovoltaic windows, intelligent textiles, and pathogen-filtering blood nanorobots. <br /> The chemical space of potential candidates for OE is vast, making the discovery and application of new materials a promising yet tedious process. Supramolecular chemistry is not only a versatile playground for new topologies but also a workhorse in the design of OE materials. Computational chemistry can accelerate the discovery timeline and enhance the understanding of underlying design principles and functionality. However, the complexity and size of OE places severe constraints on this approach. A promising solution is to combine the strengths of experimental and computational approaches in property targeted, yet broadly applicable workflows, requiring both accurate methods like density functional theory (DFT) and fast methods like semiempirical quantum-mechanical (SQM) methods and force fields (FF). This work presents several such workflows tailored to different chemical spaces and target properties in OE that are shortly introduced in the following. <br /> Artificial molecular muscles (AMMs) are a versatile subgroup within the field of molecular machines. In Chapter 1, a workflow to determine a standardized and reproducible structure model of AMMs is presented, which is then verified on a benchmark of experimentally studied AMMs. <br /> Supra- and macromolecular topological molecules are typically synthesized from smaller building blocks via C-C coupling reactions, enabling the customization of size, connectivity, and chirality. In this spirit, in Chapter 2, a workflow to examine the formation and stability of different strained anti-aromatic hoops is described and compared to experimental data. In Chapter 3, a workflow for evaluating the flexibility of molecular spoked wheels (MSWs) is presented and the feasibility of specific MSW designs is predicted. <br /> Apart from single molecules or in solution, OE materials can exist as covalent or molecular crystals with varying degrees of order, ranging from amorphous powder to aligned thin films. Covalent organic frameworks (COFs) are part of the highly structured thin film group, yet the inherent polymorphism and high porosity challenge experimental structure determination. In Chapter 4, a workflow for solid state ensemble generation of COFs is presented, aiding in the interpretation of experimentally measured structures and opto-electronic properties. <br /> Semiconducting organic molecular crystals typically exhibit intermediate charge transport rates in the hopping regime. However, accurate modeling with molecular dynamics requires the reliable evaluation of a substantial number of coupling integrals in a reasonably short time. In Chapter 5, this demand is addressed by implementing the Dimer PROjection method (DIPRO) with a density matrix tight-binding method (PTB) and extended tight-binding methods (GFN-xTB). <br /> For practical applications, molecular OE materials are often fabricated as thin films with crystal structures highly dependent on experimental conditions resulting in various polymorphs. As single-structure theoretical descriptions are insufficient and crystal structure prediction (CSP) is computationally demanding and theoretically challenging, a cluster approach is in the focus of Chapter 6. Therein, a workflow is presented based on the automated interaction site screening (aISS) using GFN-xTB methods to generate ensembles of flat and stacked aggregates. The workflow is then applied to build a novel merocyanine benchmark set and compared to experimental data.