A Semiempirical Method to Detect and Correct DFT-Based Gas-Phase Errors and Its Application in Electrocatalysis

Abstract

Computational models of adsorption at metal surfaces are often based on DFT and make use of the generalized gradient approximation. This likely implies the presence of sizable errors in the gas-phase energetics. Here, we take a step closer toward chemical accuracy with a semiempirical method to correct the gas-phase energetics of PBE, PW91, RPBE, and BEEF-vdW exchange-correlation functionals. The proposed two-step method is tested on a data set of 27 gas-phase molecules belonging to the carbon cycle: first, the errors are pinpointed based on formation energies, and second, the respective corrections are sequentially applied to ensure the progressive lowering of the data set's mean and maximum errors. We illustrate the benefits of the method in electrocatalysis by a substantial improvement of the calculated equilibrium and onset potentials for CO2 reduction to CO on Au, Ag, and Cu electrodes. This suggests that fast and systematic gas-phase corrections can be devised to augment the predictive power of computational catalysis models.