Metastable β titanium alloys are essential materials for biomaterials and aerospace applications. It is well known that their properties can be manipulated by tailoring the β phase stability and/or microstructural design, which not only results in the activation of multiple deformation mechanisms but also leads to non-homogeneous plastic deformation. Aiming at an improved understanding of the deformation mechanisms associated with the β phase stability, especially the effects of elemental distributions on the deformation mechanisms, three groups of Ti–23Nb–0.8Ta–2Zr–O (at%) alloys with varied β phase stability were produced, which correspond to the occurrence of stress induced α″ martensite transformation, mechanical twinning and slip dislocation, respectively. The β-phase stability dependence of deformation features can explain the possibility of manipulating the mechanical properties without the evident elastic properties.

In twin-dominated metal, the prevailed deformation mechanism is found to be modified into multiple twinning systems based on the dependence of crystallographic orientation. Twinned structure mainly composed of {332}<112> twin system is proposed to account for the Twin-Induced Plasticity (TWIP) effect. Thermomechanical-cycling processes are systemically applied to control a volume fraction of mechanical twins. The dissociation residual stress and passage of {332}<113>β twin can render multiple mechanical twins form into the β-phase matrix and thus induce a softening effect for enhancing uniform ductility of Ti-Nb Gum metal, attributed to well-distributed twins throughout the entire microstructure based on the ‘composite effect’.

Lastly, this paper reports on a heterogeneous-structured β-Ti alloy with an exceptional combination of high strength and ductility, resulting from optimized hierarchical features in a lamellar microstructure. The microstructure is achieved by controlling a fraction of coarse/fine domains, spatial grain-size distribution, and different types of grain boundaries. The large degree of microstructural heterogeneity leads to obvious mechanical incompatibility and strain partitioning during plastic deformation. These experimental results demonstrate that the microstructure design can be undertaken to open new possibilities to expand the material property window between the relatively low Young’s modulus related to chemical composition optimization and better formability linked to improved ductility and enhanced strain hardening.

FeAl-based Metallic-Intermetallic Laminate (MIL) composites and FeAl/FeAl2 eutectoid MIL composites of various iron alloys were fabricated with an innovative “multiple-thin-foil” configuration and “two-stage reaction” strategy. Alternating stacked metal foils were reactive sintered via spark plasma sintering (SPS) (a.k.a. field assisted sintering) to grow intermetallics. The “multiple-thin-foil” configuration reduces reaction time, enables local chemical composition control and allows metal/intermetallic combinations, which cannot be produced via the conventional methods. Fe-FeAl, 430SS-FeAl, and 304SS-FeAl MIL composites can be synthesized with desired metallic/intermetallic ratios, where FeAl is the single intermetallic phase present in the composites. The deformation and fracture evolution of the FeAl-based MIL composites is investigated here via incremental compression testing. Geometrically necessary dislocation (GND) analysis indicates the FeAl regions deform in similar manners for the three MIL composites, and each fails in a similar mode. Single-phase intermetallic FeAl layered material is also synthesized using a similar approach to study the fracture mechanisms of FeAl-based MIL composites. Mesoscale hetero-deformation induced (HDI) stress, which is tensile on the FeAl layers of MIL composites, accelerates crack nucleation and crack propagation, eventually inducing failure. The HDI stress evaluated via finite element analysis (FEA) simulation explains the difference between 430SS-FeAl and 304SS-FeAl MIL composites, which possess similar microstructure and composition, but very different strengths. Meanwhile, the electromigration effect in SPS is quantitively analyzed in the Fe-Al diffusion couple system. In SPS, the samples are heated by the applied voltage and a high electric current, which can lead to an electromigration effect. FEA simulation is utilized to determine the voltage applied to the Fe-Al diffusion couple, which is found to be extremely small to induce any voltage effect. Additionally, the simulation suggests the temperature and current density distribution is uniform across the metallic diffusion couple, which makes quantitative measurement feasible. For the first time, a mathematic algorithm, which allows diffusivity and electromigration coefficients to be solved, is developed for the system with multiple reactive layers.

Deformation of materials, especially metal alloys, is a heterogenous multiscale phenomenon. This is due to the complex synergetic effects of several different factors: macroscopic boundary conditions for the applied forces, anisotropy in the microstructure/crystallographic orientation, crystal slipping due to multiaxial stress state, non-isotropic interactions of dislocations, etc. To elucidate the complex deformation mechanism of metals and alloys, a variety of multiscale characterization techniques have been developed and applied.

Defect density calculation is established based on the Nye dislocation density tensor that relates lattice curvature measured from electron backscatter diffraction to extract geometrically necessary dislocation density. Using this method, anisotropy effects in shear localization of metals is quantified using GND density calculation in 7039 aluminum alloy (grain morphological anisotropy) and high purity titanium (crystallographic anisotropy).

The GND density characterization technique has also been used to validate Ashby’s model on dislocation type evolution by characterizing deformed nickel samples. This is the first experimental validation of Ashby’s model, which proves that the SSDs are the dominant materials’ strength contributor at higher applied loads. GNDs are only present at a larger amount in the early stage of deformation.

To increase the sensitivity of the EBSD technique, reconstruction of 2D electron backscatter diffraction patterns into spherical Kikuchi map has been explored. It potentially enables more accurate orientation calculation for GND calculation as well as improved pattern center calibration and phase analysis.

To characterize local strain distributed across the entire sample, new digital image correlation based on computer vision algorithm has been developed to show very accurate surface strain mapping of homogeneous/heterogeneously deformed materials (with artificially speckle patterns) by comparing with results obtained from open-source software. Moreover, this method also has shown improved strain sensitivity in analyzing samples using its own pattern (natural pattern), in comparison to the cross-correlation based DIC method.

Microscopic residual stress and strain are more conveniently studies using HR-EBSD method. A new HR-EBSD method is developed by employing demons registration to remap reference pattern towards pattern to solve the phantom strain problem. Additionally, the rotation, stress, and strain sensitivity have been shown to be around 0.5×10-4, 35 MPa and 2×10-4, respectively.

Bulk metallic glasses (BMGs) have the potential to exhibit exceptional wear-resistance due to their high hardness and strength. Combined with their other unique properties, this makes them ideal candidates for a wide range of technological applications (e.g. gears, bearings, biomaterials). In the course of this dissertation, high-glass-forming bulk metallic glasses are prepared and characterized in order to identify wear-resistant compositions and optimize their toughness. First, a comprehensive study identifies a class of Cu-Zr-based BMGs that exhibit more exceptional wear performance than other BMGs. The results demonstrate that when BMGs are designed properly, they exhibit wear properties that compete with, and can surpass, state-of-the-art engineering materials. It is identified that, in order to optimize the wear performance of Cu-Zr-based BMG gears, toughness should be maximized. Second, the notch toughness of wear-resistant Cu43Zr43Al7Be7 BMGs with in-situ crystallization is investigated. In order to identify in-situ crystallization using X-ray diffraction (XRD) with Cu Kα radiation, extremely long dwell times and high X-ray fluxes are required. This demonstrates the importance of reporting operating parameters when trying to evaluate the amorphous nature of BMGs. XRD, energy-dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD) are used to identify the metastable crystalline phase. The notch toughness is found to correlate closely with the amount of crystallization and the composition of the remaining amorphous matrix. Finally, the effect of substituting standard-grade zirconium lump (99.8% excluding up to 4% hafnium) for high-purity zirconium crystal bar (99.5%) in Cu43Zr43Al7Be7 is investigated. Introducing low-purity zirconium significantly decreases the glass-forming-ability and reduces the notch toughness of the BMG. Furthermore, Weibull statistics provide an analysis of the variability in toughness for high-purity ingots synthesized both in a small laboratory arc-melter and synthesized commercially. The dissertation concludes with a summary of key findings that have led to our increase in knowledge and a discussion of particularly pressing directions for future research in this field.

Transition metal nitrides and carbonitrides are used in applications ranging from extreme high-temperature environments, for their high thermal stability, to decorative coatings, for their bright golden color. Multicomponent mixtures of nitrides and carbides facilitate advantageous tunability of material properties, in some cases connected to an increase in configurational entropy. Entropic considerations for high-entropy versions of nitrides and carbonitrides are discussed, including consistent methods for calculating configurational entropy in materials with complex crystal structures and entropy thresholding for high and low-entropy materials. A new entropy metric for consistent entropy thresholding across all crystal structures is established, marking the first universal configurational entropy metric, with potential to unify the field of high-entropy materials. Eleven novel bulk high-entropy nitrides and carbonitrides are synthesized. Their composition and single-phase character are characterized using X-ray diffraction, energy dispersive x-ray spectroscopy, and combustion analysis. Their mechanical properties are analyzed using nanoindentation hardness, and a significant increase in hardness and elastic modulus above rule-of-mixtures averages is found. High-entropy carbonitrides exhibit greater hardness and modulus enhancements than the high-entropy nitrides, linked to higher configurational entropy levels and decreased valence electron concentrations, two important parameters for the tunability of mechanical properties in high-entropy ceramics. Multicomponent carbonitrides are also optimized for their optical properties. Reflectance spectra were acquired using ultraviolet-visible-near infrared spectroscopy, color quantification was obtained using CIEL*C*h* color standards, and pseudogap energies were extrapolated using Tauc analysis. Parameters for prediction and tunability of pseudogap energy and color appearance are established for multicomponent transition metal carbonitrides, ushering in a new generation of materials with advantageous optical and decorative properties as well as high, tunable hardness.

The big data revolution is only just beginning in the materials science and engineering field, offering the promise to enable high-throughput workflows and accelerate material development. For this to be realized, a new set of tools capable of using this data for identifying better material candidates and assisting in the analysis of samples must be developed. Currently, most material development projects require manually searching vast composition space while relying primarily on expert domain knowledge. While this strategy has been reasonably effective throughout history, emerging technologies are placing rigorous demands on material performance. Furthermore, many of the simplest combinations of elements (i.e. typically one primary element and one to three minor alloying elements) have been thoroughly evaluated. Within the last two decades, the materials science community has become interested in high entropy materials, typically containing five or more cations in near-equimolar amounts. This compositional space is largely unexplored and challenging to model with traditional tools, making it an excellent use case for machine learning. In the first part of the dissertation, machine learning tools are developed and demonstrated for predicting the relative synthesizability of high entropy and ultrahigh ceramics as well as the prediction of crystal structure for alloys. These machine learning approaches were validated by experiments and comparison with computational predictions where possible. The second part of the dissertation details progress in accelerating post-fabrication aspects of material design frameworks. For material analysis routines, deep neural networks were constructed to analyze diffraction patterns and determine symmetry and/or structure of the phases present. The electron backscatter diffraction (EBSD) platform was used to demonstrate the ability for such algorithms to not only identify symmetry, but that neural networks are also a potential solution to some of the grand challenges state of the art EBSD software cannot solve. This includes space group identification and the phase mapping of materials with similar crystal structures. Further validation is performed to demonstrate the model’s reliability with changing acquisition parameters. Combined, these machine learning-based tools represent an opportunity to reduce the time and expertise required for material development and are likely to become valuable components in high-throughput workflows.