STRUCTURE-ACTIVITY RELATIONSHIPS IN MOLECULAR INORGANIC CATALYSTS: A COMPUTATIONAL PERSPECTIVE

Molecular inorganic catalysts play a central role in modern catalysis, enabling selective and efficient transformations in energy conversion, environmental remediation, and fine chemical synthesis. Understanding how catalyst structure governs activity, selectivity, and stability is essential for rational catalyst design. Structure-activity relationship (SAR) analysis provides a conceptual framework for linking molecular features, such as metal identity, oxidation state, coordination geometry, ligand electronic properties, and steric environment, to catalytic performance. In recent decades, computational chemistry has emerged as a powerful tool to elucidate SARs in molecular inorganic catalysts, complementing experimental studies and enabling predictive catalyst design. Density functional theory (DFT), ab initio methods, molecular dynamics, and data-driven approaches have been widely applied to map reaction pathways, identify active species, quantify energetic trends, and reveal structure-property correlations. This review provides a comprehensive overview of computational strategies used to investigate SARs in molecular inorganic catalysis. Key structural descriptors, mechanistic insights, and representative case studies across redox, acid-base, and organometallic catalysis are discussed. Current challenges and emerging directions, including high-throughput screening and machine learning-assisted catalyst discovery, are also highlighted.