Computational mechanistic study in organometallic catalysis: Why prediction is still a challenge

Abstract Although computational contributions to the understanding of organometallic homogeneous catalysts have become fairly routine, a step‐change in the application of computational methods would be to achieve efficient, robust, and reliable prediction of the outcome of catalytic transformations. While we concur that there have been a number of recent promising advances in the interactions between computational and experimental mechanistic studies, the mapping of reactivity space remains incomplete and large‐scale studies have to make limiting assumptions which restrict their transferability. Close synergies between characterization and analysis techniques which are integrated with computational data, along with data capture, curation, and exploitation, are vital and develop our understanding of all aspects of the catalytic pathways (including activation and deactivation) and allow the continual refinement of mechanistic understanding, challenged by testing predictions experimentally. Here we review recent examples to formulate a protocol for such interactions. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Structure and Mechanism > Reaction Mechanisms and Catalysis Data Science > Artificial Intelligence/Machine Learning Electronic Structure Theory > Density Functional Theory