Automated versus Chemically Intuitive Deconvolution of Density Functional Theory (DFT)-Based Gas-Phase Errors in Nitrogen Compounds
Ricardo Urrego‐Ortiz, Santiago Builes, Federico Calle‐Vallejo
Abstract
Catalysis models involving metal surfaces and gases are regularly based on density functional theory (DFT) calculations at the generalized gradient approximation (GGA). Such models may have large errors in view of the poor DFT-GGA description of gas-phase molecules with multiple bonds. Here, we analyze three correction schemes for the PBE-calculated Gibbs energies of formation of 13 nitrogen compounds. The first scheme is sequential and based on chemical intuition, the second one is an automated optimization based on chemical bonds, and the third one is an automated optimization that capitalizes on the errors found by the first scheme. The mean and maximum absolute errors are brought down close to chemical accuracy by the third approach by correcting the inaccuracies in the NNO and ONO backbones and those in N-O and N-N bonds. This work shows that chemical intuition and automated optimization can be combined to swiftly enhance the predictiveness of DFT-GGA calculations of gases.