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Browsing by Author "Danielson, Thomas Lee"

Now showing 1 - 3 of 3
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    Investigation of Static and Dynamic Reaction Mechanisms at Interfaces and Surfaces Using Density Functional Theory and Kinetic Monte Carlo Simulations
    Danielson, Thomas Lee (Virginia Tech, 2016-05-27)
    The following dissertation is divided into two parts. Part I deals with the modeling of helium trapping at oxide-iron interfaces in nanostructured ferritic alloys (NFAs) using density functional theory (DFT). The modelling that has been performed serves to increase the knowledge and understanding of the theory underlying the prevention of helium embrittlement in materials. Although the focus is for nuclear reactor materials, the theory can be applied to any material that may be in an environment where helium embrittlement is of concern. In addition to an improved theoretical understanding of helium embrittlement, the following DFT models will provide valuable thermodynamic and kinetic information. This information can be utilized in the development of large-scale models (such as kinetic Monte Carlo simulations) of the microstructural evolution of reactor components. Accurate modelling is an essential tool for the development of new reactor materials, as experiments for components can span decades for the lifetime of the reactor. Part II of this dissertation deals with the development, and use of, kinetic Monte Carlo (KMC) simulations for improved efficiency in investigating catalytic chemical reactions on surfaces. An essential technique for the predictive development and discovery of catalysts relies on modelling of large-scale chemical reactions. This requires multi-scale modelling where a common sequence of techniques would require parameterization obtained from DFT, simulation of the chemical reactions for millions of conditions using KMC (requiring millions of separate simulations), and finally simulation of the large scale reactor environment using computational fluid dynamics. The tools that have been developed will aid in the predictive discovery, development and modelling of catalysts through the use of KMC simulations. The algorithms that have been developed are versatile and thus, they can be applied to nearly any KMC simulation that would seek to overcome similar challenges as those posed by investigating catalysis (such as the need for millions of simulations, long simulation time and large discrepancies in transition probabilities).
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    Temporal Topic Embeddings with a Compass
    Palamarchuk, Daniel Andrew (Virginia Tech, 2024-05-22)
    Aligning Word2vec word embeddings using a compass in a system of Compass-aligned Distributional Embeddings (CADE) creates stable and accurate temporal word embeddings. This thesis seeks to expand the CADE framework into the area of dynamic topic modeling (DTM), where temporal word2vec embeddings can be used to describe temporally and unsupervised evolving topics. It also seeks to improve upon the CADE framework through a theoretical and experimental exploration of compass parameters, cluster and topic generation techniques, and topic descriptor creation. This method of Temporal Topic Embeddings with a Compass (TTEC) will be compared to other DTM techniques in the ability to create coherent and diverse clusters and will be shown to be competitive compared to traditional and transformer-aided DTM architectures. In addition to a qualitative discussion of results, there will be a political theoretical overview of the nature of this technique and potential use cases, with interviews from political actors of various backgrounds as to how the technique and machine learning as a whole can be used in the organizational setting.
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    TimeLink: Visualizing Diachronic Word Embeddings and Topics
    Williams, Lemara Faith (Virginia Tech, 2024-06-11)
    The task of analyzing a collection of documents generated over time is daunting. A natural way to ease the task is by summarizing documents into the topics that exist within these documents. The temporal aspect of topics can frame relevance based on when topics are introduced and when topics stop being mentioned. It creates trends and patterns that can be traced by individual key terms taken from the corpus. If trends are being established, there must be a way to visualize them through the key terms. Creating a visual system to support this analysis can help users quickly gain insights from the data, significantly easing the burden from the original analysis technique. However, creating a visual system for terms is not easy. Work has been done to develop word embeddings, allowing researchers to treat words like any number. This makes it possible to create simple charts based on word embeddings like scatter plots. However, these methods are inefficient due to loss of effectiveness with multiple time slices and point overlap. A visualization method that addresses these problems while also visualizing diachronic word embeddings in an interesting way with added semantic meaning is hard to find. These problems are managed through TimeLink. TimeLink is proposed as a dashboard system to help users gain insights from the movement of diachronic word embeddings. It comprises a Sankey diagram showing the path of a selected key term to a cluster in a time period. This local cluster is also mapped to a global topic based on an original corpus of documents from which the key terms are drawn. On the dashboard, different tools are given to users to aid in a focused analysis, such as filtering key terms and emphasizing specific clusters. TimeLink provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.