  The structure and energy of grain boundaries (GBs) are essential for
predicting the properties of polycrystalline materials. In this work, we use
high-throughput density functional theory calculations workflow to construct
the Grain Boundary Database (GBDB), the largest database of DFT-computed grain
boundary properties to date. The database currently encompasses 327 GBs of 58
elemental metals, including 10 common twist or symmetric tilt GBs for
body-centered cubic (bcc) and face-centered cubic (fcc) systems and the
$\Sigma$7 [0001] twist GB for hexagonal close-packed (hcp) systems. In
particular, we demonstrate a novel scaled-structural template approach for HT
GB calculations, which reduces the computational cost of converging GB
structures by a factor of $\sim 3-6$. The grain boundary energies and work of
separation are rigorously validated against previous experimental and
computational data. Using this large GB dataset, we develop an improved
predictive model for the GB energy of different elements based on the cohesive
energy and shear modulus. The open GBDB represent a significant step forward in
the availability of first principles GB properties, which we believe would help
guide the future design of polycrystalline materials.
