Reaction Optimization through Mechanistic Insight and Predictive Modelling

Abstract

The control and optimization of chemical reactions lie at the heart of modern synthetic chemistry, driving progress in efficiency, selectivity, and sustainability. This review highlights the evolution of reaction optimization strategies, from empirical one-factor-at-a-time (OFAT) approaches to statistically robust methodologies based on design of experiments (DoE). These frameworks enable a systematic exploration of reaction space, providing quantitative models that accelerate process development and mechanistic understanding. The synergy between experimental and computational chemistry is discussed as a transformative paradigm for elucidating catalytic mechanisms and rationalizing selectivity in complex systems. Advances in density functional theory (DFT) and related electronic-structure analyses have enabled detailed characterization of intermediates and transition states, supporting predictive mechanistic models. Finally, the integration of machine learning (ML) into synthetic and mechanistic chemistry is outlined as a key frontier for predictive catalysis, offering new tools for reaction deployment, development, and discovery. By uniting experimental design, theoretical modeling, and data science, this multidisciplinary framework paves the way toward autonomous, data-driven reaction optimization and rational catalyst design.