Atomistic Modeling and Machine Learning for the Rational Design of Organic Energy Storage Materials
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Author(s)
Allam, Omar Adel Youssef
Advisor(s)
Lee, Seung Woo
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Abstract
The development of environmentally sustainable and cheaper alternatives to conventional inorganic materials used in ion batteries is critical for addressing both resource limitations and the environmental impact of battery production. This dissertation investigates the development of electrochemically active organic materials, which offer the potential for improved sustainability, cost-effectiveness, and fine-tuned performance. A framework integrating quantum mechanical calculations and machine learning is developed to facilitate the large-scale identification of organic materials with tailored electrochemical properties. Insights gained from this framework guide the design of carbon quantum dots exhibiting enhanced alkali-ion storage capabilities through the modulation of their functional group composition. The dissertation also investigates temperature-dependent reaction mechanisms in glyme electrolytes in CO2-containing Li-O2 batteries, offering insights into how temperature affects their stability and reactivity. Further, this work investigates strategies for prolonging the cycling stability of organic cathode materials. Specifically, to inhibit the dissolution of organic cathodes in the electrolyte, the development of novel solid polymer electrolytes is investigated. Employing molecular dynamics simulations with high-accuracy forcefields derived from quantum mechanical calculations, the effect of molecular design on nanophase morphology and ion transport are studied. This aims to establish design principles for optimizing polymer electrolytes for enhanced performance and battery stability.
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Date
2024-04-27
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Dissertation