Assessment of the 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 dataset consisting of equilibrium geometries and assessed the performance of 55 density functionals over this dataset (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 have extended this dataset with 389 new data points to include two compressed and three elongated geometries along 78 PECs for H2 binding, forming the H2Bind78x7 dataset. Assessing the performance of 55 density functionals on this significantly larger and more comprehensive H2Bind78x7 dataset, we have identified the best performing density functionals for H2 binding applications: PBE0-DH, $\omega$B97X-V, $\omega$B97M-V, and DSD-PBEPBE-D3(BJ). Addition of Hartree Fock exchange improves the performance of density functionals, albeit not uniformly throughout the PEC. We recommend the usage of wB97X-V and wB97M-V density functionals as they give good performance for both geometries and energies. In addition, we have also identified B97M-V and B97M-rV as the best semi-local density functionals for predicting H2 binding energy at its equilibrium geometry.