How Accurately Do Approximate Density Functionals Predict Trends in Acidic Zeolite Catalysis?

Density functional theory (DFT) is increasingly used for computational screening procedures with the aim of finding new catalysts. To achieve this, it is critical that relative differences between materials are predicted with high accuracy. How DFT at the generalized gradient approximation (GGA) level performs in this respect is investigated here for catalytic reactions employing acidic zeotypes using highly accurate DLPNO-CCSD(T) calculations as the reference. This is studied for 65 reaction energies and 130 reaction barriers related to zeolite catalysis. Our results obtained for the PBE-D3 and BEEF-vdW functionals show that while these functionals are prone to large errors, they predict trends occurring from one catalyst to another with an accuracy of about 5 kJ/mol, strongly supporting the widespread use of DFT calculations for the computational screening and design of new catalytic materials.