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 20 of 49 for “"materials discovery"”.
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Accelerating Materials Discovery with Machine Learning
… to leverage data to accelerate scientific discovery. This thesis focuses on how we can use historical and computational data to aid the discovery and development of new materials. We begin by looking at a traditional materials informatics task -- elucidating the structure-function …
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Physics-informed data-driven frameworks for materials discovery
This dissertation presents a comprehensive exploration of scientific machine learning methodologies applied to various aspects of material science and additive manufacturing. Chapter 2 introduces a scientific machine learning framework tailored to understand the synthesis process of flash graphene. …
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Rapid materials discovery of ternary transition metal chalcogenides
Search for new functional inorganic materials has been a perennial focus of materials science and plays a crucial role in uncovering novel phenomena. One class of materials that has particularly drawn tremendous attention is that of transition metal chalcogenides exhibiting strong d-electron …
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Materials discovery using in situ reduction and X-ray diffraction
Materials discovery is important for pushing new and existing technologies forward. In this thesis, a systematic approach to materials discovery is presented that highlights the combined use of in situ reduction reactions and X-ray diffraction to quickly explore compositional space. Reduction …
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Computational framework for metastable materials discovery: integrating kinetics and accurate properties
… crystal structure prediction has led to the discovery and experimental realization of many new materials with exceptional properties and technological promise. One of the remaining challenges in computational materials discovery is the prediction of metastable materials: long-lived materials …
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Ab Initio Anode Materials Discovery for Li- and Na-Ion Batteries
… searching method (AIRSS), to study anode materials for lithium- and sodium- ion batteries (LIBs and NIBs, respectively). Initial work relates to a theoretical structure prediction study of the lithium and sodium phosphide systems in the context of phosphorus anodes as candidates for LIBs …
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Accelerating Catalytic Materials Discovery for Sustainable Nitrogen Transformations by Interpretable Machine Learning
Computational chemistry and machine learning approaches are combined to understand the mechanisms, derive activity trends, and ultimately to search for active electrocatalysts for the electrochemical oxidation of ammonia (AOR) and nitrate reduction (NO3RR). Both re- actions play vital roles within …
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A COLLABORATIVE APPROACH TO MATERIALS DISCOVERY FOR WATER SPLITTING PHOTOCATALYSTS AND LITHIUM SULFUR REDOX.
… deployed. Starting with 70,150 compounds in the Materials Project database, the proposed protocol yielded 71 candidate photocatalysts, 11 of which were synthesized as single-phase materials. Follow up work revealed, via computation, a further 13 potential photocatalysts, which were tested for …
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Magnetic and Superconducting Materials Discovery: Employing Data Science, Natural Language Processing and Machine Learning
This thesis focusses on the application of materials informatics to the study and discovery of inorganic compounds that exhibit magnetism and superconductivity. In particular, the materials discovery process is viewed through the lens of data-mining and natural language processing, by which large …
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Accelerating Materials Discovery for Optical Applications using Machine Learning, Natural Language Processing and Density Functional Theory
… thesis presents a novel approach that combines materials informatics and theoretical calculations to accelerate the discovery of materials with desirable optical properties. Unlike traditional experimental research programmes, this work emphasises the significant contributions in terms of …
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Materials Discovery by Crystal Growth: Synthesis, Structure Determination and Physical Properties of Complex Oxides of Niobium, Tantalum, Iron and Uranium
<p>The act of materials discovery continues to grow in importance, as new materials are needed for a variety of applications ranging from solar panels to solid-state lighting. Many research groups have focused their efforts on discovering new materials, specifically oxides, where the process is …
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Automatic data interpretation from scientific literatures in the portable document format with information-extraction tools for the advancement of materials discovery
… communities, to accelerate the data-driven materials discovery. The nature of PDF files presents specific challenges for data extraction. Typically, no semantic tags are usually provided in a PDF file that is not designed to be edited or its data interpreted by software. This creates a …
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Knowledge-guided Machine Learning for Sensor-based High-Performance Autonomous Material Characterization
… characterization that drives accelerated materials discovery and manufacturing. Traditional materials discovery workflows are driven by low-throughput characterization processes that involve several manual sample preparation steps and require relatively large amounts of material. While …
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Extracting thermoelectric materials information using natural language processing
This thesis aims to empower thermoelectric materials discovery through data-driven methods. Materials discovery has been traditionally guided by the intuition of researchers and trial-and-error. This approach can be greatly accelerated by applying data-oriented methods which rely on high-quality …
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A Systematic Framework for Feature Analysis, Selection, and Property Prediction of Chemical Materials Using Machine Learning
… feature selection workflow within the field of materials informatics. Its utility is demonstrated via the prediction of various material properties across different research domains, including solid-state physics, condensed matter physics, materials science and engineering, as well as …
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Novel methods to predict solid-state material properties
Solid-state materials find ubiquitous use in modern technology - from semiconductors in electronics to steel in buildings and superconductors in MRI machines. Theoretical understanding of the atomic-scale behaviour of these materials can be leveraged to design new materials with desirable …
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First-principles control of zeolite synthesis, transformations, and intergrowth
Designing new materials enabling of sustainable catalysis and separations is essential to fully decarbonize the industrial sector, but materials discovery is hindered by labor-intensive experimentation. Computational methods such as high-throughput screening or machine learning can filter …
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Accelerating Polymer Electrolyte Discovery with Machine Learning
… in functional devices. To facilitate more rapid discovery of high ionic conductivity SPEs, we developed a chemistry-informed machine learning model that accurately predicts ionic conductivity of SPEs. To train the model, we compiled training data of SPE ionic conductivity from hundreds of …
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Spectral Analysis of Local Atomic 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 geometries of …
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Computation Aided Design Of Multicomponent Refractory Alloys With A Focus On Mechanical Properties
… processing speed has created a paradigm shift in materials discovery. Simulations can be carried out to accurately predict structure-composition-property relationships of novel systems. This work focuses on calculating elastic properties of high entropy alloys, a new class of alloys that are built …
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