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.

Increasing ubiquity of computational resources has enabled simulation of complex electronic systems and modern materials. The PAOFLOW software package is a tool designed to construct and analyze tight binding Hamiltonians from the solutions of DFT calculations. PAOFLOW leverages localized basis sets to greatly reduce computational costs of post-processing QE simulation results, enabling efficient determination of properties such as electronic density, band structures in the presence of electric or magnetic fields, magnetic or spin circular dichroism, spin-texture, Fermi surfaces, spin or anomalous Hall conductivity (SHC or AHC), electronic transport, and more. PAOFLOW's broad functionality is detailed in this work, and several independent studies where PAOFLOW's capabilities directly enabled research on promising candidates for ferroelectric and spintronic based technologies are described. Today, Quantum computers are at the forefront of computational information science. Materials scientists and quantum chemists can use quantum computers to simulate interacting systems of fermions, without having to perform the iterative methods of classical computing. This dissertation also describes a study where the band structure for silicon is simulated for the first time on quantum hardware and broadens this concept for simulating band structures of generic crystalline structures on quantum machines.

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No description available.

The current study investigates the effect of strain state, strain rate, and membrane planar area on phospholipid bilayer mechanoporation and failure. Using molecular dynamics, a 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC) bilayer was deformed biaxially to represent injury-induced neuronal membrane mechanoporation and failure. For all studies, water forming a bridge through both phospholipid bilayer leaflets was used as a failure metric. To examine the effect of strain state, 72 phospholipid structures were subjected to equibiaxial, 2:1 non-equibiaxial, 4:1 non-equibiaxial, strip biaxial, and uniaxial tensile deformations at the von Mises strain rate of 5.45 × 108 s-1. The stress magnitude, failure strain, headgroup clustering, and damage behavior were strain state dependent. The strain state order of detrimentality in descending order was equibiaxial, 2:1 non-equibiaxial, 4:1 non-equibiaxial, strip biaxial, and uniaxial with failure von Mises strains of 0.46, 0.47, 0.53, 0.77, and 1.67, respectively. Additionally, pore nucleation, growth, and failure were used to create a Membrane Failure Limit Diagram (MFLD) to demonstrate safe and unsafe membrane deformation regions. This MFLD allowed representative equations to be derived to predict membrane failure from in-plane strains. To examine the effect of strain rate, the equibiaxial and strip biaxial strain states were repeated at multiple strain rates. Additionally, a 144 phospholipid structure, which was twice the size of the 72 phospholipid structure in the x dimension, was subjected to strip biaxial tensile deformations to examine planar area effect. The applied strain rates, planar area, and cross-sectional area had no effect on the von Mises strains at which pores greater than 0.1 nm2 were detected (0.509 plus/minus 7.8%) or the von Mises strain at failure (0.68 plus/minus 4.8%). Additionally, changes in bilayer planar and cross-sectional areas did not affect the stress response. However, a strain rate increase from 1.4 × 108 to 6.8 × 108 s-1 resulted in a yield stress increase of 44.1 MPa and a yield strain increase of 0.17. Additionally, a stress and mechanoporation behavioral transition was determined to occur at a strain rate of ~1.4 × 108 s-1. These results provide the basis to implement a more accurate mechano-physiological internal state variable continuum model that captures lower-length scale damage.

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.

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<p>Co and Ni are two major elements in high temperature structural alloys that include superalloys for turbine engines and hard metals for cutting tools. The recent development of complex concentrated alloys (CCAs), loosely defined as alloys without a single principal element (e.g. CoNiFeMn), offers additional opportunities in designing new alloys through extensive composition and structure modifications. Within CCAs and Co- and Ni-based superalloys, solid-solution strengthening and stacking fault energy engineering are two of the most important strengthening mechanisms. While studied for decades, the potency and quantitative materials properties of these mechanisms remain elusive. </p> <p><br></p> <p>Solid-solution strengthening originates from stress field interactions between dislocations and solute of various species in the alloy. These stress fields can be engineered by composition modification in CCAs, and therefore a wide range of alloys with promising mechanical strength may be designed. This thesis initially reports on experimental and computational validation of newly developed theories for solid-solution strengthening in 3d transition metal (MnFeCoNi) alloys. The strengthening effects of Al, Ti, V, Cr, Cu and Mo as alloying elements are quantified by coupling the Labusch-type strengthening model and experimental measurements. With large atomic misfits with the base alloy, Al, Ti, Mo, and Cr present strong strengthening effects comparable to other Cantor alloys. </p> <p> </p> <p>Stacking fault energy engineering can enable novel deformation mechanisms and exceptional strength in face-centered cubic (FCC) materials such as austenitic TRIP/TWIP steels and CoNi-based superalloys exhibiting local phase transformation strengthening via Suzuki segregation. We employed first-principles calculations to investigate the Suzuki segregation and stacking fault energy of the FCC Co-Ni binary alloys at finite temperatures and concentrations. We quantitatively predicted the Co segregation in the innermost plane of the intrinsic stacking fault (ISF). We further quantified the decrease of stacking fault energy due to segregation.  </p> <p><br></p> <p>We further investigated the driving force of segregation and the origin of the segregation behaviors of 3d, 4d and 5d elements in the Co- and Ni-alloys. Using first-principles calculations, we calculated the ground-state solute-ISF interaction energies and revealed the trends across the periodic table. We discussed the relationships between the interaction energies and the local lattice distortions, charge density redistribution, density of states and local magnetization of the solutes. </p> <p><br></p> <p>Finally, this thesis reports on new methodologies to accelerate first-principles calculations utilizing active learning techniques, such as Bayesian optimization, to efficiently search for the ground-state energy line of the system with limited computational resources. Based on the expected improvement method, new acquisition strategies were developed and will be compared and presented. </p>