期刊
COMPUTATIONAL MATERIALS SCIENCE
卷 111, 期 -, 页码 218-230出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.commatsci.2015.09.013
关键词
High-throughput; Materials database; Scientific workflow; Directed acyclic graph; Provenance; Reproducibility
资金
- Robert Bosch LLC
- Laboratory of Theory and Simulation of Materials (THEOS)
- Swiss National Centre for Competence in Research
- Office of Science of the U.S. Department of Energy [DE-AC05-00OR22725, mat045]
- Swiss National Supercomputing Centre (CSCS) [THEOS s337, MARVEL mr1, CHRONOS ch3]
Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows. (C) 2015 Elsevier B.V. All rights reserved.
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