工作流程
计算机科学
互操作性
软件
一致性(知识库)
钥匙(锁)
系统工程
软件工程
数据库
工程类
人工智能
计算机安全
程序设计语言
操作系统
作者
Corey Oses,Marco Esters,David Hicks,Simon Divilov,Hagen Eckert,Rico Friedrich,Michael J. Mehl,Andriy Smolyanyuk,Xiomara Campilongo,Axel van de Walle,Jan Schroers,Aaron Gilad Kusne,Ichiro Takeuchi,Eva Zurek,Marco Buongiorno Nardelli,Marco Fornari,Yoav Lederer,Ohad Levy,Cormac Toher,Stefano Curtarolo A
标识
DOI:10.1016/j.commatsci.2022.111889
摘要
The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets — facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systems.
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