A Framework for Predicting Catalytic Efficiency of Metal-Organic Frameworks

Thesis Abstract

Metal-organic frameworks (MOFs) have emerged as a versatile class of materials with significant potential in catalysis due to their high surface area, tunable porosity, and structural diversity. Despite their promising applications, predicting the catalytic efficiency of MOFs remains a critical challenge, impeding the accelerated development and deployment of optimized catalysts in industrial processes. This study aims to develop a comprehensive predictive framework for assessing the catalytic efficiency of MOFs by integrating structural, electronic, and energetic parameters. The specific objectives include identifying key molecular descriptors influencing catalytic performance, establishing quantitative relationships between these descriptors and catalytic activity, and validating the framework through empirical data analysis. The research adopts a mixed-methods approach, combining quantitative modeling with empirical validation. A cross-sectional research design is employed, focusing on a representative sample of 150 MOF structures selected from existing databases such as the Cambridge Structural Database (CSD) and the Computation-Enabled Materials Database (CEMDB). These structures are chosen based on documented catalytic performances in heterogeneous reactions, including hydrogenation, oxidation, and carbon dioxide reduction. Data collection involves extracting structural information using X-ray crystallography and spectroscopy data, electronic properties via density functional theory (DFT) calculations, and kinetic parameters from published experimental studies. Complementary to computational data, laboratory validation is performed on a subset of 30 MOFs synthesized using solvothermal methods, followed by catalytic testing using standard gas-phase and liquid-phase reactions. Analytical methods encompass multiple techniques multiple linear regression (MLR) and partial least squares regression (PLSR) are utilized to establish statistical correlations between molecular descriptors and catalytic efficiency metrics such as turnover frequency (TOF) and activation energy. Principal component analysis (PCA) is applied to reduce dimensionality and identify the most influential variables. DFT computations facilitate the characterization of frontier molecular orbitals, electron density distribution, and adsorption energies, which are incorporated as variables in the predictive models. Model validation involves cross-validation techniques, residual analysis, and external validation with the experimental data from synthesized MOFs. The framework also draws upon the theoretical basis provided by the Sabatier principle and the electronic structure theory to underpin the relationships between structure and activity. Expected findings include identifying a set of key descriptors—such as pore size distribution, metal-node oxidation states, binding energies, and electron transfer capacity—that significantly influence catalytic performance. The developed models are anticipated to demonstrate high predictive accuracy (R² > 0.85) and robust generalizability across different reaction types. These models will elucidate the mechanistic links between MOF structural features and catalytic efficiency, providing a systematic tool for catalyst design. This study contributes to the body of knowledge by offering a standardized, theory-driven predictive framework that combines computational chemistry, statistical modeling, and empirical validation to forecast MOF catalytic efficiency. It advances the understanding of structure-activity relationships (SAR) in MOFs and provides a practical decision-making tool for researchers and industry practitioners aiming to tailor catalysts with desired performance characteristics. In conclusion, the findings will inform the strategic synthesis of MOFs, optimize catalytic processes, and reduce experimental trial-and-error, thereby accelerating the development of highly efficient MOF-based catalysts. Recommendations include adopting the framework for various catalytic applications, integrating machine learning algorithms for enhanced prediction, and expanding experimental validation across broader reaction conditions. Future research should focus on extending the model to multi-metallic and functionalized MOFs, as well as incorporating real-time reaction monitoring data for dynamic efficiency prediction.