  We present BEAST DB, an open-source database comprised of ab initio
electrochemical data computed using grand-canonical density functional theory
in implicit solvent at consistent calculation parameters. The database contains
over 20,000 surface calculations and covers a broad set of heterogeneous
catalyst materials and electrochemical reactions. Calculations were performed
at self-consistent fixed potential as well as constant charge to facilitate
comparisons to the computational hydrogen electrode. This article presents
common use cases of the database to rationalize trends in catalyst activity,
screen catalyst material spaces, understand elementary mechanistic steps,
analyze electronic structure, and train machine learning models to predict
higher fidelity properties. Users can interact graphically with the database by
querying for individual calculations to gain granular understanding of reaction
steps or by querying for an entire reaction pathway on a given material using
an interactive reaction pathway tool. BEAST DB will be periodically updated,
with planned future updates to include advanced electronic structure data,
surface speciation studies, and greater reaction coverage.
