BEAST DB: Grand-Canonical\nDatabase of Electrocatalyst\nProperties

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