  An in-depth insight into the chemistry and nature of the individual chemical
bonds is essential for understanding materials. Bonding analysis is thus
expected to provide important features for large-scale data analysis and
machine learning of material properties. Such chemical bonding information can
be computed using the LOBSTER software package, which post-processes modern
density functional theory data by projecting the plane wave-based wave
functions onto a local, atomic orbital basis. With the help of a fully
automatic workflow, the VASP and LOBSTER software packages are used to generate
the data. We then perform bonding analyses on 1520 compounds (insulators and
semiconductors) and provide the results as a database. The database structure
of the bonding analysis database, which allows easy data retrieval, is also
explained. The projected densities of states and bonding indicators are
benchmarked on standard density-functional theory computations and available
heuristics, respectively. Lastly, we illustrate the predictive power of bonding
descriptors by constructing a machine-learning model for phononic properties,
which shows an increase in prediction accuracies by 27 % (mean absolute errors)
compared to a benchmark model differing only by not relying on any
quantum-chemical bonding features.
