Matching Items (4)
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- All Subjects: Molecular Dynamics
- Creators: Muhich, Christopher
Description
Polyurea is a highly versatile material used in coatings and armor systems to protect against extreme conditions such as ballistic impact, cavitation erosion, and blast loading. However, the relationships between microstructurally-dependent deformation mechanisms and the mechanical properties of polyurea are not yet fully understood, especially under extreme conditions. In this work, multi-scale coarse-grained models are developed to probe molecular dynamics across the wide range of time and length scales that these fundamental deformation mechanisms operate. In the first of these models, a high-resolution coarse-grained model of polyurea is developed, where similar to united-atom models, hydrogen atoms are modeled implicitly. This model was trained using a modified iterative Boltzmann inversion method that dramatically reduces the number of iterations required. Coarse-grained simulations using this model demonstrate that multiblock systems evolve to form a more interconnected hard phase, compared to the more interrupted hard phase composed of distinct ribbon-shaped domains found in diblock systems. Next, a reactive coarse-grained model is developed to simulate the influence of the difference in time scales for step-growth polymerization and phase segregation in polyurea. Analysis of the simulated cured polyurea systems reveals that more rapid reaction rates produce a smaller diameter ligaments in the gyroidal hard phase as well as increased covalent bonding connecting the hard domain ligaments as evidenced by a larger fraction of bridging segments and larger mean radius of gyration of the copolymer chains. The effect that these processing-induced structural variations have on the mechanical properties of the polymer was tested by simulating uniaxial compression, which revealed that the higher degree of hard domain connectivity leads to a 20% increase in the flow stress. A hierarchical multiresolution framework is proposed to fully link coarse-grained molecular simulations across a broader range of time scales, in which a family of coarse-grained models are developed. The models are connected using an incremental reverse–mapping scheme allowing for long time scale dynamics simulated at a highly coarsened resolution to be passed all the way to an atomistic representation.
ContributorsLiu, Minghao (Author) / Oswald, Jay (Thesis advisor) / Muhich, Christopher (Committee member) / Jiang, Hanqing (Committee member) / Peralta, Pedro (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2020
Description
Functional materials can be characterized as materials that have tunable properties and are attractive solutions to the improvement and optimization of processes that require specific physiochemical characteristics. Through tailoring and altering these materials, their characteristics can be fine-tuned for specific applications. Computational modeling proves to be a crucial methodology in the design and optimization of such materials. This dissertation encompasses the utilization of molecular dynamics simulations and quantum calculations in two fields of functional materials: electrolytes and semiconductors. Molecular dynamics (MD) simulations were performed on ionic liquid-based electrolyte systems to identify molecular interactions, structural changes, and transport properties that are often reflected in experimental results. The simulations aid in the development process of the electrolyte systems in terms of concentrations of the constituents and can be invoked as a complementary or predictive tool to laboratory experiments. The theme of this study stretches further to include computational studies of the reactivity of atomic layer deposition (ALD) precursors. Selected aminosilane-based precursors were chosen to undergo density functional theory (DFT) calculations to determine surface reactivity and viability in an industrial setting. The calculations were expanded to include the testing of a semi-empirical tight binding program to predict growth per cycle and precursor reactivity with a high surface coverage model. Overall, the implementation of computational methodologies and techniques within these applications improves materials design and process efficiency while streamlining the development of new functional materials.
ContributorsGliege, Marisa Elise (Author) / Dai, Lenore (Thesis advisor) / Derecskei-Kovacs, Agnes (Thesis advisor) / Muhich, Christopher (Committee member) / Emady, Heather (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Titanium dioxide is an essential material under research for energy and environmental applications, chiefly through its photocatalytic properties. These properties allow it to be used for water-splitting, detoxification, and photovoltaics, in addition to its conventional uses in pigmentation and sunscreen. Titanium dioxide exists in several polymorphic structures, of which the most common are rutile and anatase. We focused on anatase for the purposes of this research, due to its promising results for hydrolysis.
Anatase exists often in its reduced form (TiO2-x), enabling it to perform redox reactions through the absorption and release of oxygen into/from the crystal lattice. These processes result in structural changes, induced by defects in the material, which can theoretically be observed using advanced characterization methods. In situ electron microscopy is one of such methods, and can provide a window into these structural changes. However, in order to interpret the structural evolution caused by defects in materials, it is often necessary and pertinent to use atomistic simulations to compare the experimental images with models.
In this thesis project, we modeled the defect structures in anatase, around oxygen vacancies and at surfaces, using molecular dynamics, benchmarked with density functional theory. Using a “reactive” forcefield designed for the simulation of interactions between anatase and water that can model and treat bonding through the use of bond orders, different vacancy structures were analyzed and simulated. To compare these theoretical, generated models with experimental data, the “multislice approach” to TEM image simulation was used. We investigated a series of different vacancy configurations and surfaces and generated fingerprints for comparison with TEM experiments. This comparison demonstrated a proof of concept for a technique suggesting the possibility for the identification of oxygen vacancy structures directly from TEM images. This research aims to improve our atomic-level understanding of oxide materials, by providing a methodology for the analysis of vacancy formation from very subtle phenomena in TEM images.
Anatase exists often in its reduced form (TiO2-x), enabling it to perform redox reactions through the absorption and release of oxygen into/from the crystal lattice. These processes result in structural changes, induced by defects in the material, which can theoretically be observed using advanced characterization methods. In situ electron microscopy is one of such methods, and can provide a window into these structural changes. However, in order to interpret the structural evolution caused by defects in materials, it is often necessary and pertinent to use atomistic simulations to compare the experimental images with models.
In this thesis project, we modeled the defect structures in anatase, around oxygen vacancies and at surfaces, using molecular dynamics, benchmarked with density functional theory. Using a “reactive” forcefield designed for the simulation of interactions between anatase and water that can model and treat bonding through the use of bond orders, different vacancy structures were analyzed and simulated. To compare these theoretical, generated models with experimental data, the “multislice approach” to TEM image simulation was used. We investigated a series of different vacancy configurations and surfaces and generated fingerprints for comparison with TEM experiments. This comparison demonstrated a proof of concept for a technique suggesting the possibility for the identification of oxygen vacancy structures directly from TEM images. This research aims to improve our atomic-level understanding of oxide materials, by providing a methodology for the analysis of vacancy formation from very subtle phenomena in TEM images.
ContributorsShindel, Benjamin Noam (Author) / Crozier, Peter (Thesis director) / Anwar, Shahriar (Committee member) / Singh, Arunima (Committee member) / Materials Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This research seeks to answer the question if there is a singular relationship between stishovite nucleation and the atomistic structure of the preshocked amorphous SiO$_2$. To do this a stishovite manufacturing method is developed in which 1,152 samples were produced. The majority of these samples did crystallize. The method was produced through two rounds of experiments and fine-tuning with the pressure damp, temperature damp, shock pressure using an NPHug fix, and sample origin. A new random atomic insertion method was used to generate a new and different SiO$_2$ amorphous structure not before seen within the research literature. The optimal values for shock were found to be 60~GPa for randomly atom insertion samples and 55~GPa for quartz origin samples. Temperature damp appeared to have a slight effect optimizing at 0.05~ps and the pressure damp had no visible effect, testing was done with temperature damp from 0.05 to 0.5~ps and pressure damp from 0.1 to 10.0~ps. There appeared to be significant randomness in crystallization behavior. The preshocked and postnucleated samples were transformed into Gaussian fields of crystal, mass, and charge. These fields were divided and classified using a cut-off method taking the number of crystals produced in portions of each simulation and classifying each potion as nucleated or non-nucleated. Data in which some nucleation but not a critical amount was present was removed constituting 2.6\% to 20.3\% of data in all tests. A max method was also used which takes only the maximum portions of each simulation to classify as nucleating. There are three other variables tested within this work, a sample size of 18,000 or 72,728~atoms, Gaussian variance of 1 or 4~\AA, and Convolutional neural network (CNN) architecture of a garden verity or all convolution along with the portioning classification method, sample origination, and Gaussian field type. In total 64 tests were performed to try every combination of variable. No significant classifications were made by the CNNs to nucleation or non-nucleation portions. The results clearly confirmed that the data was not abstracting to atomistic structure and was random by all classifications of the CNNs. The all convolution CNN testing did show smoother outcomes in training with less fluctuations. 59\% of all validation accuracy was held at 0.5 for a random state and 84\% was within $\pm0.02$ of 0.5. It is conclusive that prenucleation structure is not the sole predictor of nucleation behavior. It is not conclusive if prenucleation structure is a partial or non-factor within nucleation of stishovite from amorphous SiO$_2$.
ContributorsChristen, Jonathan Scorr (Author) / Oswald, Jay (Thesis advisor) / Muhich, Christopher (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021