Assessment of Performance of Density Functionals for Predicting Potential Energy Curves in Hydrogen Storage Applications

The
availability of accurate computational tools for modeling and
simulation is vital to accelerate the discovery of materials capable
of storing hydrogen (H2) under given parameters of pressure
swing and temperature. Previously, we compiled the H2Bind275 data
set consisting of equilibrium geometries and assessed the performance
of 55 density functionals over this data set (Veccham, S. P.; Head-Gordon,
M. J. Chem. Theory Comput. 2020, 16, 4963−4982). As it is crucial for computational tools to accurately
model the entire potential energy curve (PEC), in addition to the
equilibrium geometry, we extended this data set with 389 new data
points to include two compressed and three elongated geometries along
78 PECs for H2 binding, forming the H2Bind78 × 7 data
set. By assessing the performance of 55 density functionals on this
significantly larger and more comprehensive H2Bind78 × 7 data
set, we identified the best performing density functionals for H2 binding applications: PBE0-DH, ωB97X-V, ωB97M-V,
and DSD-PBEPBE-D3­(BJ). The addition of Hartree–Fock exchange
improves the performance of density functionals, albeit not uniformly
throughout the PEC. We recommend the usage of ωB97X-V and ωB97M-V
density functionals as they offer good performance for both geometries
and energies. In addition, we also identified B97M-V and B97M-rV as
the best semilocal density functionals for predicting H2 binding energy at its equilibrium geometry.