Modeling adsorption phenomena on surfaces by DFT calculations often involves substantial errors, resulting in inaccurate predictions of catalytic activities. Such errors partly stem from the inaccurate description of the energetics of free molecules. Herein, we use a semiempirical group-additivity method to correct the DFT-calculated heats of formation of 106 carbonand nitrogen-containing gaseous compounds belonging to 15 different chemical families. PBE, PW91, RPBE and BEEF-vdW initially yield mean absolute errors (MAEs) with respect to experiments in the range of 0.32-0.75 eV. After correcting the systematic errors, the overall MAEs decrease to~0.05 eV. Additionally, upon applying the corrections to three types of reaction enthalpies, the resulting MAEs are below 0.10 eV. These functional-group corrections can be used in (electro)catalysis to correct the gas-phase references necessary to evaluate equilibrium potentials and adsorption energies, predict error cancellation, and assess conflicting experimental data.

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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.