Analysis and Chemical Applications of Metal–Metal Bonds and Large Language Models

Advances in catalysis, energy storage, and chemical education increasingly rely on integrating molecular design, predictive modeling, and emerging technologies. Yet challenges remain in tuning metal–metal interactions, stabilizing reactive battery interfaces, and improving access to data interpretation in chemical research and education. This work addresses these challenges through a combination of synthetic chemistry, computational modeling, electrochemical analysis, and artificial intelligence (AI). Redox–active dimolybdenum paddlewheel complexes were developed to probe second–sphere charge effects, revealing a quantifiable relationship between ligand charge and Mo≣Mo redox behavior. Building on this platform, a quadruply bonded dimolybdenum–based organometallic additive was introduced into a NaPF₆/dimethyl carbonate electrolyte to improve sodium metal battery performance. This strategy addressed persistent issues with interfacial instability and dendritic growth, promoting the formation of a more uniform and stable solid electrolyte interphase and enabling improved cycling efficiency. To expand the application of data–driven molecular design, bond dissociation energies and reactivity trends in aluminum–metal complexes were modeled using density functional theory and multivariate regression. These models enabled predictive catalyst development using earth–abundant metals, offering new insight into cooperative bond activation mechanisms. Beyond molecular systems, this study also explores how artificial intelligence can support chemical practice. Large language models (LLMs) were evaluated for their effectiveness in supporting chemical safety and education. One study assessed their ability to generate accurate and relevant lab safety guidance. Another demonstrated their potential to assist students with quantitative data analysis and graphical interpretation. These findings highlight the promise of LLMs as supplemental tools when used with appropriate oversight. Together, this dissertation integrates experimental design, theoretical modeling, and emerging digital tools to tackle pressing challenges in modern chemistry. The findings provide new strategies for redox tuning, battery optimization, catalyst development, and responsible AI integration in chemical practice.