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 the 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 a 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.

中文翻译：

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BEAST DB：电催化剂特性的大规范数据库


我们提出了 BEAST DB，这是一个开源数据库，由使用大正则密度泛函理论在隐式溶剂中以一致的计算参数计算的从头计算数据组成。该数据库包含 20,000 多个表面计算，涵盖了广泛的非均相催化剂材料和电化学反应。在自洽的固定电位和恒定电荷下进行计算，以便于与计算氢电极进行比较。本文介绍了数据库的常见用例，以合理化催化剂活性的趋势，筛选催化剂材料空间，了解基本机理步骤，分析电子结构，并训练机器学习模型以预测更高保真度的属性。用户可以通过查询单个计算来获得对反应步骤的精细理解，或者通过使用交互式反应途径工具查询给定材料上的整个反应途径，从而以图形方式与数据库交互。BEAST DB 将定期更新，并计划在未来进行更新，以包括高级电子结构数据、表面形态研究和更大的反应覆盖范围。