BEAST DB: Grand-Canonical Database of Electrocatalyst Properties

TL;DR

BEAST DB is an open-source, comprehensive electrochemical catalyst database using grand-canonical DFT, enabling trend analysis, catalyst screening, mechanistic insights, electronic structure analysis, and machine learning applications.

Contribution

This work introduces BEAST DB, a large, standardized electrochemical catalyst database with interactive tools and plans for future updates, advancing catalyst research and data-driven modeling.

Findings

Contains over 20,000 surface calculations across various catalysts.

Facilitates analysis of reaction mechanisms and electronic structures.

Supports machine learning model training for property prediction.

Abstract

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.