Substantiating mechanisms in intricate catalytic cycles is a pivotal obstacle in theoretical chemistry, demanding methodologies that effectively balance precision and computational adeptness. Many computational strategies, including molecular dynamic simulations, density functional theory (DFT), and ab initio procedures, have been imprinted and polished to illuminate the steps and halfway compounds in catalytic phenomena. It is noteworthy, thus, that DFT, known for its favorable compromise between precision and computational expense, emerges as the most commonly probed computational approach for unraveling the reactions' inner geometry (Cramer, 2013). DFT allows expedient computation of possible energy points, transitional barriers, and interim structures in catalytic routes. Such calculation embraces insights into the thermodynamic propensities of each step, as well as their kinetics (Cramer, 2013). Noteworthily, the ascendancy of DFT in predicting the reactions of transition metals and organometallics/processes has been bolstered by the evolution of advanced exchange-correlation functionals and dispersion compensations, which have fortified DFT's reliability (Grimme, 2011).

It is critical to acknowledge, however, that though DFT upholds its prominence in predicting catalytic mechanisms, its precision is rather compromised due to the favorable trade-off between precision and computational costs. It is increasingly crucial to note that advanced ab initio techniques, such as CCSD(T) and MRCI, offer enhanced accuracy by systematically explicating electron correlation effects. The premise that ab initio computations are typically restricted to smaller systems or critical reaction steps remains pivotal. Notably, ab initio methods are frequently employed as benchmarking tools for validating DFT outcomes or for close observation of perplexing intermediates with multi-reference character (Sherrill, 2010). In this vein, other methodologies have emerged to be critical in investigating catalytic mechanisms. The specialized role of enzymatic catalysis executed by combining ab initio MD and hybrid quantum mechanics/molecular mechanics (QM/MM) approaches is pivotal in exploring the dynamic effects of solvent interactions and the various flexibility dimensions in catalytic systems. Perhaps, QM/MM methods should be embraced wholeheartedly to allow partitioning of the system into two domains, with one treated quantum mechanically, the other classically, for better modeling of enzymatic phenomena and heterogeneous catalysts (Senn & Thiel, 2009).

Additionally, automated reaction path search algorithms, coupled with machine learning approaches, have drastically impacted the prediction of complex catalytic reactions. These cases yield viable reaction processes without undue human intervention, allowing for the discovery of potential avenues through vast potential energy surfaces. The modern architectures of these automated algorithms and machine learning systems have transcendentally enhanced the predictive accuracy and effectiveness of catalytic route prediction (Zhou et al., 2020). This article suggests that DFT is an exemplary method for predicting the reaction mechanisms in catalysis. It outlines the need to deploy advanced ab initio techniques, such as CCSD (T) and MRCI, for close examination of the steps in catalytic processes. The article also emphasizes the role of molecular dynamic simulations, including hybrid quantum mechanics/molecular mechanics approaches, in managing dynamic effects, solvent interactions, and catalytic complexity. Finally, we see the implications of automated methods and machine learning in catalytic route prediction. Whether evaluating the precision of DFT, using advanced quantification methods to test advanced ab initio mechanisms, or embracing molecular dynamic simulations, the specificity of various scenarios and catalytic systems should be appropriately considered.

References:

Cramer, C. J. (2013). Essentials of Computational Chemistry: Theories and Models. Wiley. Grimme, S. (2011). Density functional theory with London dispersion corrections. WIREs Computational Molecular Science, 1(2), 211-228.

Senn, H. M., & Thiel, W. (2009). QM/MM methods for biomolecular systems. Angewandte Chemie International Edition, 48(7), 1198-1229.

Sherrill, C. D. (2010). Frontiers in electronic structure theory. The Journal of Chemical Physics, 132(11), 110902.

Zhou, J., Lu, X., & Hu, J. (2020). Automated reaction mechanism discovery in complex catalytic systems: Recent advances and perspectives. Chemical Reviews, 120(19), 11967-12019.

Computational methods like density functional theory (DFT) combined with microkinetic modeling effectively predict detailed reaction mechanisms in complex catalytic cycles by simulating intermediates and kinetics. Recent advances also include machine learning and path reweighting techniques to optimize catalytic activity and turnover efficiently in silico.

Density Functional Theory (DFT) is most commonly used to analyse the catalytic cycles computationally. The choice of functional and basis set is important, which provides reliable optimized geometries and some experimental comparison (if available). CCSD, as mentioned in one of the answers to this question, can be used to obtain more accurate energetics but is computationally expensive. So it is better to use functionals like hybrid-GGA for optimization, which takes care of long-range interactions and dispersion, and later perform CCSD single-point calculations.