Machine learning algorithms for atomistic systems have the potential to circumvent expensive quantum mechanical calculations and enable computations for large systems which are conventionally infeasible. In this way, the complexity of solving the many-body Schr odinger equation is reduced by mapping to statistical models. The appropriate data representation is crucial in increasing the accuracy, e ciency and reliability of the model. In this thesis, we conduct an in-depth evaluation of handcrafted and neural network learned representations for molecules, inorganic crystals and adsorbate-surface systems. In addition to evaluating the atomistic machine learning models by the mean absolute error, we employ the energy within threshold metric. We see signi cant di erences between representations from the evaluation of molecules. We propose ways to improve the performance of atomistic machine learning.

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)

To enable a generic modelling tool for precipitation kinetics in non-homogeneous components, an efficient data-handling is required to facilitate the integration of models on different length scales, and to decrease the computational time and the use of resources. In this work an automated method to generate, curate and transform Calphad- based thermodynamic and kinetic data to facilitate precipitation models integrated in FEM codes is developed and tested. The open-source Python library, pycalphad, is employed to access Calphad databases. Python scripts are utilized to calculate the thermodynamic and kinetic parameters, required to supply a precipitation model. The obtained data is stored with an open-source software infrastructure. The Cu-Co binary is the chosen model alloy in this work and the corre-sponding parameters are calculated and stored. The obtained results show, that pycalphad can be used to supply the required thermodynamic and kinetic pa- rameters for a precipitation model. Further refinement of the presented sourcecode is required to enable application in the whole composition range. / För utveckling av ett generiskt modelleringsverktyg för utskiljningskinetiken i inhomogena komponenter krävs en effektiv databehandling som möjliggör integration av modeller för olika längdskalor och minskar beräkningstiden och resursförbrukningen. I denna avhandling utvecklas och testas en automatiserad metod för att generera, kurera och transformera termodynamisk och kinetisk Calphad-data. Detta möjliggör integration av utskiljningsmodeller i finita-element metodkoder. Pycalphad tillsammans med en öppen källkod används för att komma åt Calphad-databaser. Ett Python-skript används för att beräkna de termodynamiska och kinetiska parametrarna som används i utskiljningsmodellen. Uppgifterna sparas i en öppen källkodsinfrastruktur. Den utvecklade metoden demonstreras genom att generera, kurera och transformera information för det binära modellsystemet Cu-Co Resultaten visar att Pycalphad kan användas för att tillhandahålla de nödvändiga termodynamiska och kinetiska parametrarna för utskiljningsmodeller. En ytterligare förbättring av den presenterade källkoden är nödvändig för att möjliggöra applikationen inom hela sammansättningsområdet.

abstract: A hybrid molecular dynamics (MD) simulation framework is developed to emulate mechanochemical reaction of mechanophores in epoxy-based nanocomposites. Two different force fields, a classical force field and a bond order based force field are hybridized to mimic the experimental processes from specimen preparation to mechanical loading test. Ultra-violet photodimerization for mechanophore synthesis and epoxy curing for thermoset polymer generation are successfully simulated by developing a numerical covalent bond generation method using the classical force field within the framework. Mechanical loading tests to activate mechanophores are also virtually conducted by deforming the volume of a simulation unit cell. The unit cell deformation leads to covalent bond elongation and subsequent bond breakage, which is captured using the bond order based force field. The outcome of the virtual loading test is used for local work analysis, which enables a quantitative study of mechanophore activation. Through the local work analysis, the onset and evolution of mechanophore activation indicating damage initiation and propagation are estimated; ultimately, the mechanophore sensitivity to external stress is evaluated. The virtual loading tests also provide accurate estimations of mechanical properties such as elastic, shear, bulk modulus, yield strain/strength, and Poisson’s ratio of the system. Experimental studies are performed in conjunction with the simulation work to validate the hybrid MD simulation framework. Less than 2% error in estimations of glass transition temperature (Tg) is observed with experimentally measured Tgs by use of differential scanning calorimetry. Virtual loading tests successfully reproduce the stress-strain curve capturing the effect of mechanophore inclusion on mechanical properties of epoxy polymer; comparable changes in Young’s modulus and yield strength are observed in experiments and simulations. Early damage signal detection, which is identified in experiments by observing increased intensity before the yield strain, is captured in simulations by showing that the critical strain representing the onset of the mechanophore activation occurs before the estimated yield strain. It is anticipated that the experimentally validated hybrid MD framework presented in this dissertation will provide a low-cost alternative to additional experiments that are required for optimizing material design parameters to improve damage sensing capability and mechanical properties. In addition to the study of mechanochemical reaction analysis, an atomistic model of interphase in carbon fiber reinforced composites is developed. Physical entanglement between semi-crystalline carbon fiber surface and polymer matrix is captured by introducing voids in multiple graphene layers, which allow polymer matrix to intertwine with graphene layers. The hybrid MD framework is used with some modifications to estimate interphase properties that include the effect of the physical entanglement. The results are compared with existing carbon fiber surface models that assume that carbon fiber has a crystalline structure and hence are unable to capture the physical entanglement. Results indicate that the current model shows larger stress gradients across the material interphase. These large stress gradients increase the viscoplasticity and damage effects at the interphase. The results are important for improved prediction of the nonlinear response and damage evolution in composite materials. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2017

Functional materials are essential to human welfare and to provide foundations for emerging industries. As an alternative route to experimental materials discovery, computational materials designs are playing an increasingly significant role in the whole discovery process. In this work, we use an in-house developed python utility: PAOFLOW, which generates finite basis Hamiltonians from the projection of first principles plane-wave pseudopotential wavefunctions on pseudo atomic orbitals(PAO) for post-process calculation on various properties such as the band structures, density of states, complex dielectric constants, diffusive and anomalous spin and charge transport coefficients. In particular, we calculated the dielectric function of Sr-, Pb-, and Bi-substituted BaSnO3 over wide concentration ranges. Together with some high-throughput experimental study, our result indicates the importance of considering the mixed-valence nature and clustering effects upon substitution of BaSnO3 with Pb and Bi. We also studied two prototype ferroelectric rashba semiconductors, GeTe and SnTe, and found the spin Hall conductivity(SHC) can be large either in ferroelectric or paraelectric structure phase. Upon doping, the polar displacements in GeTe can be sustained up to a critical hole concentration while the tiny distortions in SnTe vanish at a minimal level of doping. Moreover, we investigated the sensitivity of two dimensional group-IV monochalcogenides to external strain and doping, which reveal for the first time giant intrinsic SHC in these materials, providing a new route for the design of highly tunable spintronics devices based on two-dimensional materials.

Bayesian updating is used to approximate discontinuous multi-interval uncertainty representations (i.e., belief structures) of epistemic uncertainty. Several Bayesian-based approaches are examined for assessing the accuracy of approximating the mean and standard deviation of a belief structure and calculating reliability using posterior distributions. Moreover, a Bayesian-based belief structure approximation is integrated with a decomposed multilevel optimization solution strategy through analytical target cascading, where the ensuing reliability-based design optimization problem within each decomposed element is solved using a single loop single vector approach. The non-deterministic decomposed multilevel optimization approach is demonstrated through solutions to four analytical benchmark problems with mixed aleatory and epistemic uncertainties as well as a nano-enhanced composite sandwich plate problem. Consistent with the integrated computational materials engineering philosophy, the proposed solution strategy for the sandwich plate problem combines micro- and macro-level material modeling and design with structural level analysis and optimization. The orientation distribution of the carbon nanofibers in the micro-mechanical model is described through a belief structure and modeled using a Bayesian approach. Aleatory uncertainty in the ply thickness of the composite facesheets is also considered. This problem is used to demonstrate computationally efficient integration of epistemic uncertainty described through a belief structure for a complex design problem with mixed uncertainties. The results of this study show that the posterior distributions from some of the Bayesian-based approaches are suitable for direct calculation of reliability through joint probability density functions. Moreover, the Bayesian-based approach can provide a computationally efficient method for integrating epistemic and aleatory uncertainties in decomposed multilevel optimization of complex problems.

No description available.

No description available.

<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>

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.