Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 19 of 19 for “"Computational Materials Science"”.
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Adaptive heterogeneous parallelism for semi-empirical lattice dynamics in computational materials science.
… metrics that are not portable to emerging computational platforms or even alternative contemporary architectures. Furthermore, the significance of runtime concerns such as makespan, energy efficiency and fault tolerance depends on the situational context. This thesis presents a case study …
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Spectral Analysis of Local Atomic Environments
… local environments is a cornerstone challenge in computational materials science, with profound implications for property prediction and materials discovery. This thesis presents a comprehensive investigation of spectral descriptors constructed from spherical harmonic expansions to represent the …
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Learning Simple Chemical Heuristics to Model and Discover Materials
Computational approaches have long played an important role in the field of materials science, driving both the scientific study of materials’ fundamental properties and the design of materials for technological applications. Currently, mainstream methods in computational materials science …
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Theory and Practice of Large-scale Logistics: Offline Contextual Bandits and Decomposition Methods
… in health datasets, news recommendation, and computational materials science. The second part addresses large-scale logistics involving simultaneous routing and scheduling of commodity deliveries across intermodal networks. In collaboration with the United States Marine Corps and Navy, I …
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A machine learning approach to crystal structure prediction
… and applies it to binary metallic alloys. As computational materials science turns a promising eye towards design, routine encounters with chemistries and compositions lacking experimental information will demand a practical solution to structure prediction. We review the ingredients needed to …
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Extending the predictive power and scope of electronic structure theory and quantum transport
… never have predicted its eventual impact on computational materials science. Almost 50 years after his original article, the field has seen tremendous improvement both in computer hardware and in software algorithms, and the resulting combination of an elegant theory and truly predictive …
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Nanoporous graphene as a water desalination membrane
… Thanks to significant advances in the field of computational materials science in the past decade, it is becoming possible to develop a new generation of RO membranes. In this thesis, we explore how computational approaches can be employed to understand, predict and ultimately design a future …
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Enhancing Robustness of Neural Network Interatomic Potentials through Sampling Methods and Uncertainty Quantification
… (NNIPs) are a significant advancement in computational materials science and chemistry for their ability to accurately approximate the potential energy surface (PES) of atomic systems with significantly reduced computational costs compared to quantum mechanical methods. Without relying on …
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Towards Machine Learning Foundation Models for Materials Chemistry
… how recent advances in machine learning (ML) for materials can accelerate our search for new stable inorganic crystals. We show how best to measure and compare the utility of different models, what range of applications a foundational ML force field can be expected to cover, what remaining …
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Static and Dynamic Disorder in Emerging Optoelectronic Materials
… The resulting scientific narrative that emerging materials are bearers of novel technologies has led to the discovery of manifold new semiconducting material types which significantly differ from the above materials in structure and arising charge-carrier species. It remains the task of current …
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Computational electronic structure studies of novel condensed matter phases
… in the field of theoretical chemistry and materials science: I employed high-performance computing tools to perform electronic structure investigations of novel crystalline materials synthesized, some for the very first time, in the group. My placement in the experimentally-focused Jain …
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Determination of the Raman Spectra of Molten ZnCl2 and Thermophysical Properties of Polymerized C60 Solids Using Atomistic Computational Techniques
… technology have enabled the implementation of computational materials science algorithms to examine the structure-property relations of a wide variety of materials. Fundamental insights gained from these studies can thus provide guidelines for the appropriate selection of materials (or …
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Spin-Aware Neural Network Interatomic Potential for Atomistic Simulation
Computational modeling is key in materials science for developing mechanistic insight that enables new applications. ab initio methods capture exceptional phenomenological richness to high numerical accuracy, but at high cost and limited scale. Empirical potentials are faster and scale better, but …
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A first principles study of defects in titanium: interaction of twin boundaries with dislocations and oxygen interstitials
Interaction between gliding dislocations and twin boundaries affects the plastic deformation of hcp metals such as titanium. In addition, oxygen greatly affects both strength and twinning in titanium. Predictive models of strength and twinning rely on understanding of the underlying atomic scale …
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Accelerating Material Inverse Design in High-Dimensional Continuous Spaces with Autonomous Machine Learning Ecosystems
Modeling and designing materials at the nanoscale is essential for advancing new technologies in crucial areas such as energy, electronics, and catalysis. By gaining control over structure at the atomic level, we can now pursue inverse design, where the goal is to start from a desired property and …
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First-principles study of magnetic ground and excited-state properties of metallic antiferromagnets
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-14 without embargo terms
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Characterizing the potential energy surface of two dimensional and bulk materials using high dimensional neural network potentials
… properties at the ab-initio level of detail is computationally prohibitive for large systems or long timescales. As a result, such methods cannot be used to efficiently sample configuration space. Force field methods can efficiently sample configuration space, but rely on large parameter sets …
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Atomic Modelling of Disorder in Metal Nanocrystals
… (MSD,  ̄(σ_i^2 ) ) is often used in computational materials science studies to calculate measurable properties from the atomic trajectories of simulations; for example, the diffusion coefficient, which according to Einstein relations (Einstein 1905) on the random walk is 1/6 of the …
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Data-Driven Research for Amorphous Materials: Towards Seamless Utilization of Publication Data in Chemical Sciences
… standing challenges in the research on amorphous materials have been identified. In particular, the lack of reliable data repositories for properties and structures of amorphous materials significantly limits the possibilities for research. As a consequence, state-of-the-art data-driven methods, …