Assessing the Accuracy of Density Functional Approximations for Predicting Hydrolysis Reaction Kinetics

Hydrolysis reactions are ubiquitous in biological, environmental,and industrial chemistry. Density functional theory (DFT) is commonlyemployed to study the kinetics and reaction mechanisms of hydrolysisprocesses. Here, we present a new data set, Barrier Heights for HydrOlysis- 36 (BH2O-36), to enable the design of density functional approximations(DFAs) and the rational selection of DFAs for applications in aqueouschemistry. BH2O-36 consists of 36 diverse organic and inorganic forwardand reverse hydrolysis reactions with reference energy barriers Delta E (double dagger) calculated at the CCSD-(T)/CBS level.Using BH2O-36, we evaluate 63 DFAs. In terms of mean absolute error(MAE) and mean relative absolute error (MRAE), omega B97M-V is thebest-performing DFA tested, while MN12-L-D3-(BJ) is the best-performingpure (nonhybrid) DFA. Broadly, we find that range-separated hybridDFAs are necessary to approach chemical accuracy (0.043 eV). Althoughthe best-performing DFAs include a dispersion correction to accountfor long-range interactions, we find that dispersion corrections donot generally improve MAE or MRAE for this data set.

Automated summary

Hydrolysis reactions are widely present in biological, environmental, and industrial chemistry. Density functional theory (DFT) is commonly used to study the kinetics and reaction mechanisms of hydrolysis processes. This study introduces a new data set, BH2O-36, and evaluates different DFAs for their performance in aqueous chemistry applications.