Towards More Powerful Materials Microstructure Simulation: Extension to Soft Matter Systems and Acceleration by Machine Learning

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dc.contributor.advisorTang, Mingen_US
dc.creatorFan, Shaoxunen_US
dc.date.accessioned2024-08-30T16:36:54Zen_US
dc.date.created2024-08en_US
dc.date.issued2024-07-16en_US
dc.date.submittedAugust 2024en_US
dc.date.updated2024-08-30T16:36:54Zen_US
dc.descriptionEMBARGO NOTE: This item is embargoed until 2025-08-01en_US
dc.description.abstractThis thesis explores two significant advancements in modern materials science: simulation techniques and the application of machine learning. Simulations enable scientists to not only predict the properties of new materials but also gain insights into the behavior and underlying mechanisms of existing ones. Machine learning can further enhance materials research in numerous ways. In this thesis, we first present our phase-field simulation of micelles and emulsion, followed by the application of deep learning models to accelerate simulations and analyze simulation results. A comprehensive understanding of the morphological transformations of micelles and emulsions can benefit various fields. However, direct and especially in-situ observation of these systems faces certain limitations. In the first topic of this thesis, we investigate the formation and transformation mechanisms of micelles and emulsions at mesoscale using phase-field simulation. The two-stage growth of micelle cluster during fission was discovered. In the first stage, the volume of micelles grows exponentially while in the second stage, the cluster growth is limited by long-range transport of surfactant monomers. A quantitative model explaining the observed cluster growth kinetics was then established. The phase-field model was extended to water-oil-surfactant ternary system to enable the study of emulsion's morphological transition. Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. In the second topic of the thesis, we present a computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. In the third topic, we study the potential of recurrent convolutional neural network models in regressing parameters from spatial and temporal features in image sequences generated by simulations, showing machine learning's capability in automatically extracting information that are critical for understanding behaviors of various materials. It provides a novel data-driven approach for materials research and analysis.en_US
dc.embargo.lift2025-08-01en_US
dc.embargo.terms2025-08-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationFan, Shaoxun. Towards More Powerful Materials Microstructure Simulation: Extension to Soft Matter Systems and Acceleration by Machine Learning. (2024). PhD diss., Rice University. https://hdl.handle.net/1911/117798en_US
dc.identifier.urihttps://hdl.handle.net/1911/117798en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectmicrostructure evolutionen_US
dc.subjectphase-field modelingen_US
dc.subjectmachine learningen_US
dc.titleTowards More Powerful Materials Microstructure Simulation: Extension to Soft Matter Systems and Acceleration by Machine Learningen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentMaterials Science and NanoEngineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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