Phase-field (PF) models are one of the most powerful tools to simulate microstructural evolution in metallic materials, polymers, and ceramics. However, existing PF approaches rely on rigorous mathematical model development, sophisticated numerical schemes, and high-performance computing for accuracy. Although recently developed surrogate microstructure models employ deep-learning techniques and reconstruction of microstructures from lower-dimensional data, their accuracy is fairly limited as spatio-temporal information is lost in the pursuit of dimensional reduction. Given these limitations, a novel data-driven emulator (DDE) for extrapolation prediction of microstructural evolution is presented, which combines an image-based convolutional and recurrent neural network (CRNN) with tensor decomposition, while leveraging previously obtained PF datasets for training. To assess the robustness of DDE, the emulation sequence and the scaling behavior with phase-field simulations for several noisy initial states are compared. In conclusion, the effectiveness of the microstructure emulation technique is explored in the context of accelerating runtime, along with an emphasis on its trade-off with accuracy.Meanwhile, an interpolation DDE has also been tested, which is based on obtaining a low-dimensional representation of the microstructures via tensor decomposition and subsequently predicting the microstructure evolution in the low-dimensional space using Gaussian process regression (GPR). Once the microstructure predictions are obtained in the low-dimensional space, a hybrid input-output phase retrieval algorithm will be employed to reconstruct the microstructures. As proof of concept, the results on microstructure prediction for spinodal decomposition are presented, although the method itself is agnostic of the material parameters. Results show that GPR-based DDE model are able to predict microstructure evolution sequences that closely resemble the true microstructures (average normalized mean square of 6.78 × 10−7) at time scales half of that employed in obtaining training data. This data-driven microstructure emulator opens new avenues to predict the microstructural evolution by leveraging phase-field simulations and physical experimentation where the time resolution is often quite large due to limited resources and physical constraints, such as the phase coarsening experiments previously performed in microgravity. Future work will also be discussed and demonstrate the intended utilization of these two approaches for 3D microstructure prediction through their combined application.

Solid-state and non-equilibrium processings are of great interest to researchers due to their ability to control and refine bulk and/or surface microstructure of metallic alloys and push them to surpass their conventional properties limit. In this dissertation, solid-state processing i.e., Shear Assisted Processing and Extrusion (ShAPE), and non-equilibrium processes i.e., surface mechanical attrition (SMAT) and additive manufacturing (AM) techniques were used to process the magnesium and aluminum alloys respectively. A synergistic investigation of processing-induced microstructural modification and its effect on corrosion resistance was performed using various ex-situ, quasi in-situ, and in-situ electrochemical, microscopy, and spectroscopy characterization techniques. To evaluate the effect of the same processing condition on a range of microstructures, a variety of magnesium alloys such as AZ31B, Mg-3Si, ZK60, and Pure Mg were processed using a novel solid-state processing method, namely ShAPE. It induced a significant grain refinement, homogenized distribution of second phases, and low residual strain in AZ31B alloy, which contributed toward a noble breakdown potential, stable protective film, and hence better corrosion resistance compared to the parent extruded counterpart. However, with variations in composition, volume fraction, and distribution of second phases with Mg-3Si and ZK60 magnesium alloy an opposite response was inferred indicating a strong dependence of corrosion on underlying microstructure compared to a processing condition. Non-equilibrium processes, i.e. SMAT and AM were utilized to process high-strength 7xxx series aluminum alloys. Continuous high energy impacts of hard balls in room temperature (RT SMAT) and liquid nitrogen (LN2 SMAT) flow environment generated a gradient nanocrystalline surface layer with the dissolution of inherent second phase and precipitation of new phases in aluminum 7075 alloys. RT SMAT showed a reduced anodic dissolution rate and improved film resistance, which was attributed to the thicker and composite oxide layer along with new nanoscale precipitates. Lastly, reactive AM was used to process aluminum 7075 and 7050 alloys which resulted in a refined and textureless microstructure. A reduction in corrosion resistance was observed with precipitation of excessive reactive particles (Ti and B4C) in AM alloys compared to wrought counterparts.

Electromigration, the net atomic diffusion associated with the momentum transfer from electrons moving through a material, is a major cause of device and component failure in microelectronics. The deleterious effects from electromigration rise with increased current density, a parameter that will only continue to increase as our electronic devices get smaller and more compact. Understanding the dynamic diffusional pathways and mechanisms of these electromigration-induced and propagated defects can further our attempts at mitigating these failure modes. This dissertation provides insight into the relationships between these defects and parameters of electric field strength, grain boundary misorientation, grain size, void size, eigenstrain, varied atomic mobilities, and microstructure.First, an existing phase-field model was modified to investigate the various defect modes associated with electromigration in an equiaxed non-columnar microstructure. Of specific interest was the effect of grain boundary misalignment with respect to current flow and the mechanisms responsible for the changes in defect kinetics. Grain size, magnitude of externally applied electric field, and the utilization of locally distinct atomic mobilities were other parameters investigated. Networks of randomly distributed grains, a common microstructure of interconnects, were simulated in both 2- and 3-dimensions displaying the effects of 3-D capillarity on diffusional dynamics. Also, a numerical model was developed to study the effect of electromigration on void migration and coalescence. Void migration rates were found to be slowed from compressive forces and the nature of the deformation concurrent with migration was examined through the lens of chemical potential. Void migration was also validated with previously reported theoretical explanations. Void coalescence and void budding were investigated and found to be dependent on the magnitude of interfacial energy and electric field strength. A grasp on the mechanistic pathways of electromigration-induced defect evolution is imperative to the development of reliable electronics, especially as electronic devices continue to miniaturize. This dissertation displays a working understanding of the mechanistic pathways interconnects can fail due to electromigration, as well as provide direction for future research and understanding.

With the abundance of increasingly large datasets, the ability to predict the phase of high-entropy alloys (HEAs) based solely on elemental composition could become a reliable tool for the discovery of new HEAs. However, as the amount of data expands so does the computational time and resources required to train predictive classical machine learning models. Quantum computers, which use quantum bits (qubits), could be the solution to overcoming these demands. Their ability to use quantum superposition and interference to perform calculations could be the key to handling large amounts of data. In this work, a hybrid quantum-classical machine learning algorithm is implemented on both quantum simulators and quantum processors to perform the supervised machine learning task. Their feasibility as a future tool for HEA discovery is evaluated based on the algorithm’s performance. An artificial neural network (ANN), run by classical computers, is also trained on the same data for performance comparison. The accuracy of the quantum-classical model was found to be comparable to the accuracy achieved by the classical ANN with a slight decrease in accuracy when ran on quantum hardware due to qubit susceptibility to decoherence. Future developments in the applied quantum machine learning method are discussed.

Physical vapor deposition (PVD) of phase-separating multicomponent alloy films generates a rich variety of unique self-organized nanoscale morphologies. However, an understanding of how the different material and process parameters influence the formation of these nanostructures is limited. My dissertation aims to bridge this gap by developing phase-field models that can predict an entire spectrum of nanostructures as a function of processing conditions and composition in multicomponent alloys under a set of material-specific constraints. Firstly, I developed a numerical model to simulate nanoscale phase separation in codeposited immiscible binary alloy films. An investigation on the influence of deposition rates, phase-fraction, and temperature, on the evolution of self-assembled nanostructures yielded many characteristic patterns, including well-known morphologies such as the lateral and vertical concentration modulations, as well as some previously undocumented variants. I also simulated phase-separation in ternary alloyed PVD films, and studied the influence of deposition rate and composition on the evolution of self-assembled nanostructures, and recorded many novel nanoscale morphologies. I then sought to understand the role of material properties such as elastic misfit due to lattice mismatch between phases, grain boundaries formed in polycrystalline films, and the interplay of interphase and surface boundaries at the film surface. To this end, I developed phase-field models of binary PVD film deposition that incorporated these constraints and studied their role in altering the temporal and spatial characteristics of the evolving morphologies. I also investigated the formation of surface hillocks and the role of surface and interfacial energies in their evolution. By studying the change in total free energy across the different deposition models, I established that, in addition to influencing the temporal and spatial characteristics of nanoscale structures in the films, this quantity is also responsible for driving morphological transitions as the rate of deposition is increased. Insights gained from this computational study will demonstrate the viability of these models in predicting experimentally observed morphologies and form a basis for understanding the various factors involved in driving phase-separation and morphological transitions. In addition, morphology maps will serve as templates for developing new pathways for morphology control in the manufacturing of PVD alloy films.

AA 7XXX alloys are used extensively in aircraft and naval structures due to their excellent strength to weight ratio. These alloys are often exposed to harsh corrosive environments and mechanical stresses that can compromise their reliability in service. They are also coupled with fasteners that are composed of different materials such as Titanium alloys. Such dissimilar metal contact facilitates galvanic and crevice corrosion, which can further reduce their lifetimes. Despite decades of research in the area, the confluence of mechanical, microstructural, and electrochemical aspects of damage is still unclear. Traditionally, 2D and destructive methods have often been employed to study the corrosion and cracking behavior in these systems which can be severely limiting and lead to inaccurate conclusions. This dissertation is aimed at comprehensively studying the corrosion and cracking behavior of these systems using time-dependent 3D microstructural characterization, as well as correlative microscopy. The microstructural evolution of corrosion in AA 7075 was studied using a combination of potentiodynamic polarization, X-ray Computed Tomography (XCT) and Transmission X-ray Microscopy (TXM). In both experiments, a strong emphasis was placed on studying localized corrosion attack at constituent particles and intergranular corrosion. With an understanding of the alloy’s corrosion behavior, a dissimilar alloy couple comprising AA 7075 / Ti-6Al-4V was then investigated. Ex situ and in situ x-ray microtomography was used extensively to investigate the evolution of pitting corrosion and corrosion fatigue in AA 7075 plates fastened separately with Ti-6Al-4V screws and rivets. The 4D tomography combined with the extensive fractography yielded valuable information pertaining the preferred sites of pit initiation, crack initiation and growth in these complex geometries. The use of correlative microscopy-based methodologies yielded multimodal characterization results that provided a unique and seminal insight on corrosion mechanisms in these materials.

Miniaturization of microdevices comes at the cost of increased circuit complexity and operating current densities. At high current densities, the resulting electron wind imparts a large momentum to metal ions triggering electromigration which leads to degradation of interconnects and solder, ultimately resulting in circuit failure. Although electromigration-induced defects in electronic materials can manifest in several forms, the formation of voids is a common occurrence. This research aims at understanding the morphological evolution of voids under electromigration by formulating a diffuse interface approach that accounts for anisotropic mobility in the metallic interconnect. Based on an extensive parametric study, this study reports the conditions under which pancaking of voids or the novel void ‘swimming’ regimes are observed. Finally, inferences are drawn to formulate strategies using which the reliability of interconnects can be improved.

Phononic crystals are artificially engineered materials that can forbid phonon propagation in a specific frequency range that is referred to as a “phononic band gap.” Phononic crystals that have band gaps in the GHz to THz range can potentially enable sophisticated control over thermal transport with “phononic devices”. Calculations of the phononic band diagram are the standard method of determining if a given phononic crystal structure has a band gap. However, calculating the phononic band diagram is a computationally expensive and time-consuming process that can require sophisticated modeling and coding. In addition to this computational burden, the inverse process of designing a phononic crystal with a specific band gap center frequency and width is a challenging problem that requires extensive trial-and-error work.

In this dissertation, I first present colloidal nanocrystal superlattices as a new class of three-dimensional phononic crystals with periodicity in the sub-20 nm size regime using the plane wave expansion method. These calculations show that colloidal nanocrystal superlattices possess phononic band gaps with center frequencies in the 102 GHz range and widths in the 101 GHz range. Varying the colloidal nanocrystal size and composition provides additional opportunities to fine-tune the phononic band gap. This suggests that colloidal nanocrystal superlattices are a promising platform for the creation of high frequency phononic crystals.

For the next topic, I explore opportunities to use supervised machine learning for expedited discovery of phononic band gap presence, center frequency and width for over 14,000 two-dimensional phononic crystal structures. The best trained model predicts band gap formation, center frequencies and band gap widths, with 94% accuracy and coefficients of determination (R2) values of 0.66 and 0.83, respectively.

Lastly, I expand the above machine learning approach to use machine learning to design a phononic crystal for a given set of phononic band gap properties. The best model could predict elastic modulus of host and inclusion, density of host and inclusion, and diameter-to-lattice constant ratio for target center and width frequencies with coefficients of determinations of 0.94, 0.98, 0.94, 0.71, and 0.94 respectively. The high values coefficients of determination represents great opportunity for phononic crystal design.

Materials have been the backbone of every major invention in the history of mankind, e.g. satellites and space shuttles would not exist without advancement in materials development. Integral to this, is the development of nanocrystalline (NC) materials that promise multitude of properties for advanced applications. However, they do not tend to preserve structural integrity under intense cyclic loading or long-term temperature exposures. Therefore, it is imperative to understand factors that alter the sub-features controlling both structural and functional properties under extreme conditions, particularly fatigue and creep. Thus, this dissertation systematically studies the tensile creep and fatigue behaviour of a chemically optimized and microstructurally stable bulk NC copper (Cu)-3at.% tantalum (Ta) alloy.

Strategic engineering of nanometer sized clusters of Ta into the alloy’s microstructure were found to suppress the microstructure instability and render remarkable improvement in the high temperature tensile creep resistance up to 0.64 times the melting temperature of Cu. Primary creep in this alloy was found to be governed by the relaxation of the microstructure under the applied stress. Further, during the secondary creep, short circuit diffusion of grain boundary atoms resulted in the negligible steady-state creep rate in the alloy. Under fatigue loading, the alloy showed higher resistance for crack nucleation owing to the inherent microstructural stability, and the interaction of the dislocations with the Ta nanoclusters. The underlying mechanism was found to be related to the diffused damage accumulation, i.e., during cyclic loading many grains participate in the plasticity process (nucleation of discrete grain boundary dislocations) resulting in homogenous accumulation rather than localized one as typically observed in coarse-grained materials. Overall, the engineered Ta nanoclusters were responsible for governing the underlying anomalous high temperature creep and fatigue deformation mechanisms in the alloy.

Finally, this study presents a design approach that involves alloying of pure metals in order to impart stability in NC materials and significantly enhance their structural properties, especially those at higher temperatures. Moreover, this design approach can be easily translated to other multicomponent systems for developing advanced high-performance structural materials.

As the microelectronics industry continues to decrease the size of solder joints, each joint will have to carry a greater current density, making atom diffusion due to current flow, electromigration (EM), a problem of ever-increasing severity. The rate of EM damage depends on current density, operating temperature, and the original microstructure of the solder joint, including void volume, grain orientation, and grain size. While numerous studies have investigated the post-mortem effects of EM and have tested a range of current densities and temperatures, none have been able to analyze how the same joint evolves from its initial to final microstructure. This thesis focuses on the study of EM, thermal aging, and thermal cycling in Sn-rich solder joints. Solder joints were either of controlled microstructure and orientation or had trace alloying element additions. Sn grain orientation has been linked to a solder joints’ susceptibility to EM damage, but the precise relationship between orientation and intermetallic (IMC) and void growth has not been deduced. In this research x-ray microtomography was used to nondestructively scan samples and generate 3D reconstructions of both surface and internal features such as interfaces, IMC particles, and voids within a solder joint. Combined with controlled fabrication techniques to create comparable samples and electron backscatter diffraction (EBSD) and energy-dispersive spectroscopy (EDS) analysis for grain orientation and composition analysis, this work shows how grain structure plays a critical role in EM damage and how it differs from damage accrued from thermal effects that occur simultaneously. Unique IMC growth and voiding behaviors are characterized and explained in relation to the solder microstructures that cause their formation and the possible IMC-suppression effects of trace alloying element addition are discussed.