Photonic properties depend on both dielectric contrast in a microscopic composite and the arrangement of the microstructural components. No theory exists for direct prediction of photonic properties, and so progress relies on numerical methods combined with insight into manufacturable composite architectures. We present a discussion of effective photonic crystal production techniques and several numerical methods to predict dispersion relations of hypothetical but fabricable structures. / Singapore-MIT Alliance (SMA)

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The primary goals of the research in this dissertation are to perform a systematic study to identify and understand the fundamental cause of prolonged processing time during transient liquid phase bonding of difficult-to-bond single crystal Ni-base materials, and use the acquired knowledge to develop an effective way to reduce the isothermal solidification time without sacrificing the single crystalline nature of the base materials. To achieve these objectives, a multi-scale numerical modeling approach, that involves the use of a 2-D fully implicit moving-mesh Finite Element method and a Cellular Automata method, was developed to theoretically investigate the cause of long isothermal solidification times and determine a viable way to minimize the problem. Subsequently, the predictions of the theoretical models are experimentally validated. Contrary to previous suggestions, numerical calculations and experimental verifications have shown that enhanced intergranular diffusivity has a negligible effect on solidification time in cast superalloys and that another important factor must be responsible. In addition, it was found that the concept of competition between solute diffusivity and solubility as predicted by standard analytical TLP bonding models and reported in the literature as a possible cause of long solidification times is not suitable to explain salient experimental observations. In contrast, however, this study shows that the problem of long solidification times, which anomalously increase with temperature is fundamentally caused by departure from diffusion controlled parabolic migration of the liquid-solid interface with holding time during bonding due to a significant reduction in the solute concentration gradient in the base material. Theoretical analyses showed it is possible to minimize the solidification time and prevent formation of stray-grains in joints between single crystal substrates by using a composite powder mixture of brazing alloy and base alloy as the interlayer material, which prior to the present work has been reported to be unsuitable. This was experimentally verified and the use of the composite powder mixture as interlayer material to reduce the solidification time and avoid stray-grain formation during TLP bonding of single crystal superalloys has been reported for the first time in this research.

The primary goals of the research in this dissertation are to perform a systematic study to identify and understand the fundamental cause of prolonged processing time during transient liquid phase bonding of difficult-to-bond single crystal Ni-base materials, and use the acquired knowledge to develop an effective way to reduce the isothermal solidification time without sacrificing the single crystalline nature of the base materials. To achieve these objectives, a multi-scale numerical modeling approach, that involves the use of a 2-D fully implicit moving-mesh Finite Element method and a Cellular Automata method, was developed to theoretically investigate the cause of long isothermal solidification times and determine a viable way to minimize the problem. Subsequently, the predictions of the theoretical models are experimentally validated. Contrary to previous suggestions, numerical calculations and experimental verifications have shown that enhanced intergranular diffusivity has a negligible effect on solidification time in cast superalloys and that another important factor must be responsible. In addition, it was found that the concept of competition between solute diffusivity and solubility as predicted by standard analytical TLP bonding models and reported in the literature as a possible cause of long solidification times is not suitable to explain salient experimental observations. In contrast, however, this study shows that the problem of long solidification times, which anomalously increase with temperature is fundamentally caused by departure from diffusion controlled parabolic migration of the liquid-solid interface with holding time during bonding due to a significant reduction in the solute concentration gradient in the base material. Theoretical analyses showed it is possible to minimize the solidification time and prevent formation of stray-grains in joints between single crystal substrates by using a composite powder mixture of brazing alloy and base alloy as the interlayer material, which prior to the present work has been reported to be unsuitable. This was experimentally verified and the use of the composite powder mixture as interlayer material to reduce the solidification time and avoid stray-grain formation during TLP bonding of single crystal superalloys has been reported for the first time in this research.

Predicting new materials through virtually screening a large number of hypothetical materials using supercomputers has enabled materials discovery at an accelerated pace. However, the innumerable number of possible hypothetical materials necessitates the development of faster computational methods for speedier screening of materials reducing the time of discovery. In this thesis, I aim to understand and apply two computational methods for materials prediction. The first method deals with a computational high-throughput study of superalloys. Superalloys are materials which exhibit high-temperature strength. A combinatorial high-throughput search across 2224 ternary alloy systems revealed 102 potential superalloys of which 37 are brand new, all of which we patented. The second computational method deals with a machine-learning (ML) approach and aims at understanding the consistency among five different state-of-the-art machine-learning models in predicting the formation enthalpy of 10 different binary alloys. The study revealed that although the five different ML models approach the problem uniquely, their predictions are consistent with each other and that they are all capable of predicting multiple materials simultaneously.My contribution to both the projects included conceiving the idea, performing calculations, interpreting the results, and writing significant portions of the two journal articles published related to each project. A follow-up work of both computational approaches, their impact, and future outlook of materials prediction are also presented.

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In den letzten Jahren hat sich maschinelles Lernen als hilfreiches Werkzeug zur Vorhersage von simulierten Materialeigenschaften erwiesen. Somit können aufwendige Berechnungen mittels Dichtefunktionaltheorie umgangen werden und bereits bekannte Materialien besser verstanden oder sogar neuartige entdeckt werden. Eine zentrale Rolle spielt dabei der Deskriptor, ein möglichst interpretierbarer Satz von Materialkenngrößen. Diese Arbeit präsentiert einen Ansatz zur Auffindung von Deskriptoren für periodische Multikomponentensysteme, deren Eigenschaften durch die genaue atomare Anordnung mitbeinflusst wird. Primäre Features von Einzel-, Paar- und Tetraederclustern werden über die Superzelle gemittelt und weiter algebraisch kombiniert. Aus den so erzeugten Kandidaten wird mittels Dimensionalitätsreduktion ein geeigneter Deskriptor identifiziert. Zudem stellt diese Arbeit Strategien vor bei der Modellfindung Kreuzvalidierung einzusetzen, sodass stabilere und idealerweise besser generalisierbare Deskriptoren gefunden werden. Es werden außerdem mehrere Fehlermaße untersucht, die die Qualität der Deskriptoren bezüglich Genauigkeit, Komplexität der Formeln und Berücksichtung der atomaren Anordnung charakterisieren. Die allgemeine Methodik wurde in einer teilweise parallelisierten Python-Software implementiert. Als konkrete Problemstellungen werden Modelle für die Gitterkonstante und die Mischenergie von ternären Gruppe-IV Zinkblende-Legierungen "gelernt", mit einer Genauigkeit von 0.02 Å bzw. 0.02 eV. Datenbeschaffung, -analyse, und -bereinigung werden im Hinblick auf die Zielgrößen als auch auf die primären Features erläutert, sodass umfassende Analysen und die Parametrisierung der Methodik an diesem Testdatensatz durchgeführt werden können. Als weitere Anwendung werden Gitterkonstante und Bandlücken von binären Oktett-Verbindungen vorhergesagt. Die präsentierten Deskriptoren werden mit den Fehlermaßen evaluiert und ihre physikalische Relevanz wird abschließend disktutiert. / In the last years, machine learning methods have proven as a useful tool for the prediction of simulated material properties. They may replace effortful calculations based on density functional theory, provide a better understanding of known materials or even help to discover new materials. Here, an essential role is played by the descriptor, a desirably interpretable set of material parameters. This PhD thesis presents an approach to find descriptors for periodic multi-component systems where also the exact atomic configuration influences the physical characteristics. We process primary features of one-atom, two-atom and tetrahedron clusters by an averaging scheme and combine them further by simple algebraic operations. Compressed sensing is used to identify an appropriate descriptor out from all candidate features. Furthermore, we develop elaborate cross-validation based model selection strategies that may lead to more robust and ideally better generalizing descriptors. Additionally, we study several error measures which estimate the quality of the descriptors with respect to accuracy, complexity of their formulas and the capturing of configuration effects. These generally formulated methods were implemented in a partially parallelized Python program. Actual learning tasks were studied on the problem of finding models for the lattice constant and the energy of mixing of group-IV ternary compounds in zincblende structure where an accuracy of 0.02 Å and 0.02 eV is reached, respectively. We explain the practical preparation steps of data acquisition, analysis and cleaning for the target properties and the primary features, and continue with extensive analyses and the parametrization of the developed methodology on this test case. As an additional application we predict lattice constants and band gaps of octet binary compounds. The presented descriptors are assessed quantitatively by the error measures and, finally, their physical meaning is discussed.

<p dir="ltr">Advancements in material science are accelerating technological evolution, driven by initiatives like the Materials Genome Project, which integrates computational and experi- mental strategies to expedite material discovery. In this work, we focus on the reliability of advanced materials under extreme conditions, a critical area for enhancing their technological applications.</p><p dir="ltr">Multi-principal component alloys (MPEAs) exhibit remarkable properties under extreme conditions. However, their vast compositional space makes a brute-force exploration of potential alloys prohibitive. We address this challenge by employing a Bayesian approach to explore the oxidation resistance of hundreds of alloys, applying computational techniques to accurately calculate and quantify errors in the melting temperatures of MPEAs, and investigating the compositional biases and short-range order in their nucleation behaviors.</p><p dir="ltr">Furthermore, we scrutinize the role of wide bandgap semiconductors, which are essential in high-power applications due to their superior breakdown voltage, drift velocity, and sheet charge density. The lack of lattice-matched substrates often results in strained films, which enhances piezoelectric effects crucial for device reliability. Our research advances the pre- diction of piezoelectric and dielectric responses as influenced by biaxial strain and doping in gallium nitride (GaN). Additionally, we delve into how various common defects affect the formation of trap states, significantly impacting the electronic properties of these materials. These studies offer significant advancements in understanding MPEAs and wide bandgap semiconductors under extreme conditions. We also provide foundational insights for developing robust and efficient materials essential for next-generation applications.</p>