Date of Award
Doctor of Philosophy (PhD)
Dr. Fadi Abdeljawad
Dr. Murray Daw
Dr. Gang Li
Dr. Enrique Martinez Saez
Nearly all structural and functional materials are polycrystalline alloys; they are composed of differently oriented crystalline grains that are joined at internal interfaces termed grain boundaries (GBs). It is well accepted that GB dynamics play a critical role in many phenomena during materials processing or under operating environments. Of particular interest are GB migration and grain growth processes, as they influence many crystal-size dependent properties, such as mechanical strength and electrical conductivity.
In metallic alloys, GBs offer a plethora of preferential atomic sites for alloying elements to occupy. Indeed, recent experimental studies employing in-situ microscopy revealed strong GB solute segregation in a wide range of engineering alloys. With regards to the migration of doped GBs, solute segregation influences boundary dynamics in two main mechanisms. The first is a thermodynamic effect described by the Gibbs adsorption equation; the segregation of elemental species to a GB at a given temperature and pressure reduces the boundary free energy and, as a result, the driving force for GB migration. The second mechanism is kinetic, termed solute drag. Segregated solutes attempt to remain within the GB region and, as a result, the migrating boundary has to drag solute atoms.
While interface solute segregation has been an area of active research, most existing treatments focus on the thermodynamic aspect of GB segregation, and the kinetic role is not well understood. Here, we present a theoretical GB solute drag model in regular solution alloys, which explicitly accounts for solute-solute interactions in both the bulk and GBs and captures various transport mechanisms. Further, the model incorporates the impact of GB structure on segregation profiles. Our analysis shows that GB solute-solute interactions play a critical role in drag effects. Moreover, our theoretical analysis suggests a self-similar behavior for the GB drag-velocity maps. We then employ the model to elucidate the impact of GB geometry on solute drag in a model Pt-Au alloy, which has been shown to exhibit sluggish grain growth even at elevated temperatures. To circumvent the computational costs of performing solute drag and grain growth simulations for various input parameters, we implement physics-informed neural networks that not only predict solute drag values but also satisfy the underlying physics as described by our model.
Our theoretical and computational modeling framework is material agnostic-it is applicable to a wide range of metallic alloys. Scientific knowledge gained from our studies has the potential to facilitate a paradigm shift in the development of advanced metallic alloys by enabling an interface by design approach, in which the interactions of alloying elements with GBs are explicitly accounted for in models of microstructural evolution.
Alkayyali, Malek, "Mesoscale Modeling and Machine Learning Studies of Grain Boundary Segregation in Metallic Alloys" (2023). All Dissertations. 3329.
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