Browsing by Subject "Particle Swarm Optimization"
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Item Computational Study and Design of Self-Assembling Block Polymers(2023-01) Case, LoganUpon cooling below the order-disorder transition temperature, block polymers self-assemble into a wide variety of nanostructured morphologies. When paired with advances in synthetic chemistry that allow unprecedented control over the size and architecture of these block polymers, these self-assembly characteristics make block polymers excellent candidates for use in specialty materials with highly tunable properties. Potential applications of block polymers range from filtration membranes to photonic crystals. As it happens, however, the source of this exemplary potential is also one of the great barriers to its realization. The vast design spaces available for block polymers (through numbers and permutations of chemistries, and architectural features) make possible a potentially limitless variety of morphologies. At the same time, these design spaces combined with the subtlety of mechanisms driving morphology selection make finding systems which adopt those morphologies a daunting task.In this dissertation, we take a computational approach to address the challenge of designing block polymer specialty materials through two broad approaches. First, we directly address the challenges posed by these vast design spaces by developing an open-source software to automate the exploration of polymer parameter space. This software uses a particle swarm optimization algorithm to guide a search through polymer parameter space for positions where self-consistent field theory predicts a targeted morphology will be most stable compared to a set of competing phases. Second, we use computational studies of two classes of diblock blends seeking to understand the mechanisms that stabilize the low-symmetry Frank-Kasper phases in block polymers with the goal of improving the intuition that guides future efforts to design block polymer materials. In the first of these studies, we use an AB/B`C diblock ``alloy'' with miscible corona and immiscible core blocks to probe the effect of conformational asymmetry on the stability of Frank- Kasper Laves phases when the conformational asymmetry is confined to only particular particle positions. This study finds that conformational asymmetry can be either stabilizing or destabilizing for the Laves phases, depending on which particles are impacted. In the second of these studies, we attempt to identify the balance of core and corona bidispersity in AB/A`B` blends which can still enable formation of Frank-Kasper phases. Unfortunately, this latter study was complicated by a series of methodological flaws limiting its utility in the furtherance of block polymer design. Regardless, the flawed study serves as a lesson in proper study design, and the importance of carefully considering complicating factors.Item Optimization Algorithms for Spatially Targeted Deep Brain Stimulation(2017-12) Peña, EdgarOptimization algorithms hold significant promise for precision medicine. This dissertation focuses on the application of optimization algorithms to improve the efficacy and efficiency of deep brain stimulation (DBS) therapy for treating brain disorders. Targeting of DBS therapy for a given patient involves neurosurgical implantation of one or more leads of electrodes within the brain and then identifying a set of electrode configurations and stimulation amplitudes that most robustly suppress clinical symptoms. One approach to improve spatial targeting of DBS therapy has been the development of DBS leads with multiple electrodes positioned around and along the lead implant. However, with the additional number of electrodes, brute-force determination of stimulation settings that can most selectively modulate the brain pathways of interest becomes a time-consuming challenge. This is especially relevant since DBS can also induce adverse side effects such as involuntary muscle contractions and mood changes if stimulation is not delivered correctly. In this dissertation, I will show how convex and particle swarm optimization techniques using multi-objective contexts can be applied to subject-specific computational models of DBS to address this challenge. These optimization algorithms leverage complex bioelectric tissue models as well as detailed anatomical and biophysical computational models of the motor thalamus for treating Essential Tremor and the subthalamic nucleus region for treating Parkinson’s disease. Both convex and particle swarm optimization demonstrated robust and efficient performance in generating spatially targeted solutions consisting of non-trivial combinations of active electrodes and stimulation amplitudes. These optimization algorithms have important applications for both targeting specific brain pathways to understand their role in behavior as well as in helping clinicians reduce the dimensionality of the stimulation parameter feature space to evaluate.