Predictive catalysis to induce experimental catalytic efficiency

ENG-Predictive chemistry is emerging as a key tool for the development of new reactions and catalysts. This field combines computational models with experiments to design and optimize chemical processes in a rational way, anticipating results before laboratory testing. Despite recent progress, important challenges remain, such as reliably linking predictions with experimental data, applying these methods to more complex systems, and developing new strategies for molecular design. This thesis addresses these challenges by applying predictive chemistry to a wide range of catalytic reactions, including both metal-catalyzed and metal-free systems. It begins with a review of predictive catalysis principles, recent trends, and the conceptual framework underlying the work. Computational studies provide detailed insights into reaction mechanisms and trends in reactivity, demonstrating that models can generate reliable predictions while complementing experiments for validation and refinement. Building on this foundation, inverse design strategies are implemented to identify molecular structures with optimal properties for specific reactions. Computational screening and property-based optimization algorithms expand the design space and accelerate the discovery of efficient catalysts. Finally, predictive chemistry methodologies are extended to heterogeneous systems, such as surfaces and solid materials, illustrating the versatility and broad applicability of these tools across different catalytic environments. The results of this thesis highlight how combining theoretical models and experimental data can guide the discovery and development of new catalysts, offering opportunities for innovation in chemistry and industrial processes. Moreover, the thesis demonstrates that predictive chemistry not only enhances understanding of chemical processes but also enables the design and prediction of reactivity more efficiently, accelerating research and reducing experimental resource use