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  1. Coarse-grained, semiflexible copolymer behavior with applications to heterochromatin

    MacPherson, Quinn Jerome
    [Stanford, California] : [Stanford University], 2020

    The properties of polymer materials can vary widely and depend on their organization and the identity and interactions of their monomers. By requiring many monomers to occupy adjacent spatial locations, the connectivity of a polymer amplifies relatively weak monomer interactions. In this thesis I use theoretical and computational techniques to predict the resulting phase behavior and structure. These techniques use the wormlike chain model to incorporate the structural rigidity of the polymer backbone and use coarse-graining to capture the interactions. A semiflexible polymer backbone coupled with a repulsive Flory-Huggins interaction between blocks of a random copolymer causes frustration in the microphase segregation of the monomer types. The resulting phase diagram and structures can be adjusted by varying the statistics of the random sequence and the polymer rigidity. When the interactions depend on the orientation rather than identity of the monomers, a liquid crystal phase forms. I calculate the Frank elastic constants for liquid crystal solutions of wormlike chains that interact via a Maier-Saupe type interaction. A particular focus is devoted to the application of polymer physics techniques to the biological system of chromatin. An HP1-H3K9me3 interaction model of heterochromatin is found to be capable of explaining features of Hi-C data and aid in the interpretation of ChIP-seq data. This interaction is used to provide a density-based physical explanation for the prevalence of peripheral heterochromatin

  2. Computational approach toward rational device engineering of organic photovoltaics

    Lee, Franklin Langlang
    [Stanford, California] : [Stanford University], 2018.

    Organic photovoltaics (OPVs) have emerged as a promising alternative to conventional PV technology due to their low cost and industry-level scalability with high-volume production through solution-based processing. OPVs combine the unique flexibility and versatility of plastics with electronic properties, making them amenable to applications in the "Internet of Things" and distributed generation applications. The current key challenge for wide adaptation of OPVs is the lack of high power conversion efficiency (PCE) in large scale roll-to-roll processed devices. A key factor is the morphology: there exists disorder between the electron donor and electron acceptor materials in the active layer, and the mechanisms by which the morphology can be tuned are not well understood. Simulation is a promising inexpensive technique for exploring OPVs in the large parameter space of both processing methods and chemical components. In this work, we leverage and improve upon these computational approaches to reduce the need for iterative design for OPVs. First, we develop a multiscale molecular dynamics (MD) model to provide understanding of morphology evolution during solution processing. In addition, we train and utilize a predictive deep learning model to study the correlation of performance with the chemical and engineering design considerations. These parallel approaches allow for an accelerated sampling of the parameter space of OPV conditions, which in turn leads to targeted experiments.

  3. Insights into chemical reactions at interfaces from enhanced sampling and global optimization algorithms

    Ludwig, Thomas Kris
    [Stanford, California] : [Stanford University], 2021

    Many important technologies and current scientific challenges involve chemical reactions that occur in complex environments, such as surfaces/interfaces or condensed phases which have complex structures and dynamics under reaction conditions. Engineering catalysts and processes for these reactions depends on developing an understanding of the mechanisms that determine the rates of these reactions. Applying computational modeling to catalytic reactions is challenging for several reasons; my work demonstrates several strategies for modeling increasingly complex chemical reactions. Accurate theoretical study of catalytic interfaces and solvent effects has been an active area of research for decades, as it requires simultaneously including detailed electronic structure and chemisorption effects from the catalyst and the many intermolecular interactions and long-range electrostatic interactions from the solvent, electrolyte or other surrounding environment, all of which must be sampled over an ensemble of configurations. I address this challenge by systematically applying global optimization algorithms to models of solvent-metal interfaces. I explain trends in adsorbate-electrolyte interactions and relate these to the adsorbate dipole moment and hydrogen bonding affinity, and shed new light on models of ion promotion effects. And I extend these methods to study the interfaces of nonaqueous solvents with transition metals, uncovering several new insights into the effects of solvent chemisorption on a variety of metal surfaces. The effect of temperature on the reaction free energies and barriers of elementary chemical reactions on surfaces is often calculated using the harmonic approximation. This is due to the computational expense of first principles calculations and the simplicity of the harmonic approximation. Often these methods rely on system-specific intuition and assumptions regarding which configurations and types of anharmonicities may be important. More rigorous, scalable and efficient free energy and enhanced sampling methods would be useful in addressing this challenge. In this thesis I describe applying a state-of-the art machine learning based enhanced sampling method to a density functional theory (DFT) model of a prototypical surface reaction. This method calculates the free energy profile of the reaction more efficiently (fewer calculations required) than other currently widely used free energy methods, while exploring and identifying the critical states in the reaction mechanism. I also apply metadyamics to study nitrogen dissociation in lithium, demonstrating the use of an enhanced sampling algorithm for the exploration of reaction mechanisms and calculation of rate constants of a surface reaction. This work is a step toward systematic, generalizable methods for the computational study of chemical reactions in complex environments


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