The Materials Genome Initiative was announced by the White House in June of 2011, and is a multi-agency initiative which calls the materials community to find ways to discover, develop, manufacture, and deploy advanced materials systems faster and more cost-efficiently. Currently, the amount of time it takes to discover and develop a new material system, optimize its properties, integrate it in to a system, certify that system, and develop the manufacturing capability so that it can be deployed in a commercial component takes at least 20 years. Since this trend holds regardless of the material system in question, the implication is that it is the process by which we as a community move through these seven steps, which causes the lengthy timeline. Historically, the discovery, development, and property optimization of a material system relies heavily on deep scientific knowledge, intuition and trial-and-error physical experimentation. Therefore much of the design and testing of materials in these early stages is currently performed through time-consuming and repetitive experimental and characterization feedback loops. Some of these feedback loops could be eliminated in the property optimization step with improved powerful and accurate computational modeling tools. However, while the ability of computational models to be used in this way is not new, models that have been developed in this space have consistently underperformed. Oftentimes, these models fail because they fail to accurately account for the various physical and chemical mechanisms that are driving the system, or because they fail to account for all of the variables which must be included. Here we propose a standard method of communication for these relationships in the form a process-structure-property-performance map, which leverages the known knowledge database of the material system to clearly and visually communicate the relevant variables and their various relationships in a defined materials design space. Such a map is developed here for high-strength Al-Zn-Mg-Cu alloys, which offer a good example of a material system which could benefit from such a standard. This class of alloys, which are typically utilized in aircraft components, have been incorporated in commercial components for nearly 75 years, and due to its long history is a well characterized and well developed system that is highly suited to this kind of examination. In Part I of this work, we develop this standard by first examining the known knowledge database in this system to deduce what the important process, microstructure, and mechanical property variables are that are of interest. Once these variables and the relationships between them are identified, they are organized into a PSPP map according to a proposed set of steps, and can act as a visual standard that can clearly communicate critical information about the mechanisms of the system. For example, if a model developed within this system does not include a variable or a mechanism depicted within the map, it can be used to communicate the ways in which the model will be constrained. Similarly, when experimental data is collected within this space the map can be used to clearly communicate which variables in the space were held constant, which variables were tracked and accurately measured, and if any variables were unaccounted for. This information can help to communicate what situations the data can be used in, and how the space that the experimental data can be used in is constrained. In Part II of this work, we vary multiple parameters within the high-strength Al-Zn-Mg-Cu system defined in Part I, and attempted to track and measure as many of the variables within the space as possible using commonly available testing and characterization methods. In tackling such a large project in the complicated materials system of high-strength wrought Al-Zn-Mg-Cu alloys, we are able to understand which current testing and characterization methods are well suited to tracking these variables when the number of test specimens becomes quite large and when variability among those specimens is involved. We are also able to identify opportunities for future work in this area, which could be focused on improving our ability to implement projects of the scope that is required here. In addition to evaluating the feasibility of the various measurement and characterization methods, the raw data and the analyzed results for this work are cataloged in an associated data repository and have been made available for use in future work in this and other areas.

Many grand societal challenges are rooted in the need for new materials, such as those related to energy, health, and the environment. However, the traditional way of discovering new materials is basically trial and error. This time-consuming and expensive method can't meet the quickly growing requirements for material discovery. To meet this challenge, the government of the United States started the Materials Genome Initiative (MGI) in 2011. MGI aims at accelerating the pace and reducing the cost of discovering new materials. The success of MGI needs materials innovation infrastructure including data tools, computation tools, and experiment tools. The last decade has witnessed significant progress for MGI, especially with respect to hard materials. However, relatively less attention has been paid to soft materials. One important reason is the lack of experimental tools, especially characterization tools for high-throughput experimentation. This dissertation aims to enrich the toolbox by trying new sensor tools for high-throughput characterization of hydrogels. Piezoelectric-excited millimeter-sized cantilever (PEMC) sensors were used in this dissertation to characterize hydrogels. Their capability to investigate hydrogels was first demonstrated by monitoring the synthesis and stimuli-response of composite hydrogels. The PEMC sensors enabled in-situ study of how the manufacturing process, i.e. bulk vs. layer-by-layer, affects the structure and properties of hydrogels. Afterwards, the PEMC sensors were integrated with robots to develop a method of high-throughput experimentation. Various hydrogels were formulated in a well-plate format and characterized by the sensor tools in an automated manner. High-throughput characterization, especially multi-property characterization enabled optimizing the formulation to achieve tradeoff between different properties. Finally, the sensor-based high-throughput experimentation was combined with active learning for accelerated material discovery. A collaborative learning was used to guide the high-throughput formulation and characterization of hydrogels, which demonstrated rapid discovery of mechanically optimized composite glycogels. Through this dissertation, we hope to provide a new tool for high-throughput characterization of soft materials to accelerate the discovery and optimization of materials. / Doctor of Philosophy / Many grand societal challenges, including those associated with energy and healthcare, are driven by the need for new materials. However, the traditional way of discovering new materials is based on trial and error using low throughput computational and experimental methods. For example, it often takes several years, even decades, to discover and commercialize new materials. The lithium-ion battery is a good example. Traditional time-consuming and expensive methods cannot meet the fast-growing requirements of modern material discovery. With the development of computer science and automation, the idea of using data, artificial intelligence, and robots for accelerated materials discovery has attracted more and more attention. Significant progress has been made in metals and inorganic non-metal materials (e.g., semiconductors) in the past decade under the guidance of machine learning and the assistance of automated robots. However, relatively less progress has been made in materials having complex structures and dynamic properties, such as hydrogels. Hydrogels have wide applications in our daily lives, such as drugs and biomedical devices. One significant barrier to accelerated discovery and engineering of hydrogels is the lack of tools that can rapidly characterize the material's properties. In this dissertation, a sensor-based approach was created to characterize the mechanical properties and stimuli-response of soft materials using low sample volumes. The sensor was integrated with a robot to test materials in high-throughput formats in a rapid and automated measurement format. In combination with machine learning, the high-throughput characterization method was demonstrated to accelerate the engineering and optimization of several hydrogels. Through this dissertation, we hope to provide new tools and methods for rapid engineering of soft materials.

AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties. In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys.

Magnesium alloys are being developed as advanced materials for structural applications where reduced weight is a primary motivator. Alloying can enhance the properties of magnesium without significantly affecting its density. Essential to alloy development, inclusive of processing parameters, is knowledge of thermodynamic, kinetic, and mechanical behavior of the alloy and its constituents. Appreciable progress has been made through conventional development processes, but to accelerate development of suitable wrought Mg alloys, an integrated Materials Genomic approach must be taken where thermodynamics and diffusion kinetic parameters form the basis of alloy design, process development, and properties-driven applications. The objective of this research effort is twofold: first, to codify the relationship between diffusion behavior, crystal structure, and mechanical properties; second, to provide fundamental data for the purpose of wrought Mg alloy development. Together, the principal deliverable of this work is an advanced understanding of Mg systems. To that end, the objective is accomplished through an aggregate of studies. The solid-to-solid diffusion bonding technique is used to fabricate combinatorial samples of Mg-Al-Zn ternary and Mg-Al, Mg-Zn, Mg-Y, Mg-Gd, and Mg-Nd binary systems. The combinatorial samples are subjected to structural and compositional characterization via Scanning Electron Microscopy with X-ray Energy Dispersive Spectroscopy, Electron Probe Microanalysis, and analytical Transmission Electron Microscopy. Interdiffusion in binary Mg systems is determined by Sauer-Freise and Boltzmann-Matano methods. Kirkaldy*s extension of the Boltzmann-Matano method, on the basis of Onsager*s formalism, is employed to quantify the main- and cross-interdiffusion coefficients in ternary Mg solid solutions. Impurity diffusion coefficients are determined by way of the Hall method. The intermetallic compounds and solid solutions formed during diffusion bonding of the combinatorial samples are subjected to nanoindentation tests, and the nominal and compositionally dependent mechanical properties are extracted by the Oliver-Pharr method. In addition to bolstering the scantly available experimental data and first-principles computations, this work delivers several original contributions to the state of Mg alloy knowledge. The influence of Zn concentration on Al impurity diffusion in binary Mg(Zn) solid solution is quantified to impact both the pre-exponential factor and activation energy. The main- and cross-interdiffusion coefficients in the ternary Mg solid solution of Mg-Al-Zn are reported wherein the interdiffusion of Zn is shown to strongly influence the interdiffusion of Mg and Al. A critical examination of rare earth element additions to Mg is reported, and a new phase in thermodynamic equilibrium with Mg-solid solution is identified in the Mg-Gd binary system. It is also demonstrated that Mg atoms move faster than Y atoms. For the first time the mechanical properties of intermetallic compounds in several binary Mg systems are quantified in terms of hardness and elastic modulus, and the influence of solute concentration on solid solution strengthening in binary Mg alloys is reported. The most significant and efficient solid solution strengthening is achieved by alloying Mg with Gd. The Mg-Nd and Mg-Gd intermetallic compounds exhibited better room temperature creep resistance than intermetallic compounds of Mg-Al. The correlation between the concentration dependence of mechanical properties and atomic diffusion is deliberated in terms of electronic nature of the atomic structure.