  Efficient and high capacity storage materials are indispensable for a
hydrogen-based economy. In silico tools can accelerate the process of discovery
of new adsorbent materials with optimal hydrogen adsorption enthalpies. Density
functional theory is well-poised to become a very useful tool for enabling
high-throughput screening of potential materials. In this work, we have
identified density functional approximations that provide good performance for
hydrogen binding applications following a two-pronged approach. First, we have
compiled a dataset (H2Bind275) that comprehensively represents the hydrogen
binding problem capturing the chemical and mechanistic diversity in the binding
sites encountered in hydrogen storage materials. We have also computed
reference interaction energies for this dataset using coupled cluster theory.
Secondly, we have assessed the performance of 55 density functional
approximations for predicting H$_2$ interaction energies and have identified
two hybrid density functionals ($\omega$B97X-V and $\omega$B97M-V), two double
hybrid density functionals (DSD-PBEPBE-D3(BJ) and PBE0-DH), and one semi-local
density functional (B97M-V) as the best performing ones. We have recommended
the addition of empirical dispersion corrections to systematically underbinding
density functionals like revPBE, BLYP, and B3LYP for improvements in
performance at negligible additional cost. We have also recommended the usage
of the def2-TZVPP basis set as it represents a good compromise between accuracy
and cost, limiting the finite basis set errors to less than 1kJ/mol.
