The expense of quantum chemistry calculations significantly hinders the
search for novel catalysts. Here, we provide a tutorial for using an
easy and highly cost-efficient calculation scheme called alchemical
perturbation density functional theory (APDFT) for rapid predictions of
binding energies of reaction intermediates and reaction barrier heights
based on Kohn-Sham density functional theory reference data. We outline
standard procedures used in computational catalysis applications,
explain how computational alchemy calculations can be carried out for
those applications, and then present bench marking studies of binding
energy and barrier height predictions. Using a single OH binding energy
on the Pt(111) surface as a reference case, we use computational alchemy
to predict binding energies of 32 variations of this system with a mean
unsigned error of less than 0.05 eV relative to single-point DFT
calculations. Using a single nudged elastic band calculation for
CH dehydrogenation on Pt(111) as a reference case, we
generate 32 new pathways with barrier heights having mean unsigned
errors of less than 0.3 eV relative to single-point DFT calculations.
Notably, this easy APDFT scheme brings no appreciable computational cost
once reference calculations are done, and this shows that simple
applications of computational alchemy can significantly impact
DFT-driven explorations for catalysts. To accelerate computational
catalysis discovery and ensure computational reproducibility, we also
include Python modules that allow users to perform their own
computational alchemy calculations.