AiiDA-defects：一个自动化和完全可重复的工作流程，用于功能材料中缺陷化学的完整表征
AiiDA-defects: An automated and fully reproducible workflow for the complete characterization of defect chemistry in functional materials
摘要：
在我们的日常生活中能够实现许多技术应用的功能材料将其独特的性能归功于在加工过程中精心设计并融入这些材料中的缺陷。然而，优化和表征这些缺陷在实践中是非常具有挑战性的，使得计算建模成为不可或缺的补充工具。我们已经开发了一个自动化的工作流程和代码来加速这些计算（AiiDA缺陷），它利用了AiiDA框架，这是一个强大的开源高通量材料信息学基础设施，可提供工作流程自动化，同时保留和存储完整的数据来源于可查询和可遍历的关系数据库。本文介绍了AiiDA缺陷的设计和实施细节，所使用的模型和算法，并演示了其在应用程序中的使用，以充分表征众所周知的固态锂离子导体LiZnPS 4的缺陷化学。我们预计，AiiDA缺陷将作为一种全自动和可重复的缺陷计算工具，允许以可靠和高通量的方式获得详细的缺陷化学，并为加速材料设计和发现的缺陷数据库的生成铺平道路。
英文摘要：
Functional materials that enable many technological applications in our everyday lives owe their unique properties to defects that are carefully engineered and incorporated into these materials during processing. However, optimizing and characterizing these defects is very challenging in practice, making computational modelling an indispensable complementary tool. We have developed an automated workflow and code to accelerate these calculations (AiiDA-defects), which utilises the AiiDA framework, a robust open-source high-throughput materials informatics infrastructure that provides workflow automation while simultaneously preserving and storing the full data provenance in a relational database that is queryable and traversable. This paper describes the design and implementation details of AiiDA-defects, the models and algorithms used, and demonstrates its use in an application to fully characterize the defect chemistry of the well known solid-state Li-ion conductors LiZnPS 4 . We anticipate that AiiDA-defects will be useful as a tool for fully automated and reproducible defect calculations, allowing detailed defect chemistry to be obtained in a reliable and high-throughput way, and paving the way toward the generation of defects databases for accelerated materials design and discovery
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
