When 3:00 PM - 4:30 PM Jul 18, 2023
Where 1017 HH Dow or virtual
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PhD defense: "Enabling Controlled Material Synthesis and Processing via Predictive Modeling and Simulation"

Guanglong Huang
Thornton group


Because the structures of materials at both atomic and microstructural scales greatly affect material properties, it is critical to understand the effect of processing conditions on the resulting material structures in order to obtain a material with desired properties. Computational models that can predict materials’ evolution under synthesis and processing conditions, along with complementary experimental characterizations, are powerful tools that provide insights into the process designs for materials with tailored properties. This dissertation presents a collection of computational models and methods that enable predictions of material structures under different conditions and sample temperature control during experiments. 

The first part of this dissertation describes a set of computational models and methods developed to simulate phase transformations, microstructure evolution, and relaxation of atomic structures during material synthesis and processing. A phase-field model that captures the evolution of ionic concentrations and phase fractions during solid-state metathesis reactions was first presented. This model was employed to investigate the effect of mobilities of ions on the reaction dynamics, to predict the phase evolutions during the solid-state metathesis reaction for the synthesis of FeS2, as well as to study the effect of particle packing density on the reaction rate. Two phase-field models that describe the evolution of microstructure with dislocations were then discussed. A simple model assuming a uniform intra-grain dislocation density was employed to study the macroscale translation of grains during non-isothermal annealing. An extended model that considers intra-grain dislocation density variation was utilized to study the effect of cyclic heat treatment on the microstructure evolution. Finally, the relaxation of flat and buckled triangular monolayers of atoms was achieved using a phase-field-crystal model. 

The second part of this dissertation presents heat transfer models designed to assist in sample temperature control during experiments, as well as a machine learning algorithm developed to automatically determine uncertain parameters for computational models. First, a heat transfer model that describes the temperature distribution within a sample in an optical floating zone experiment was discussed. A machine learning algorithm, which iteratively refines the range of uncertain parameters, was developed. The effectiveness of the algorithm was demonstrated by applying it to determine uncertain parameters in the heat transfer model for the optical floating zone experiment. Additionally, a coupled thermal and Joule heating model for a gradient heater furnace was presented, which was used to study the geometry of the heater on the resulting thermal profiles within the sample.