AiiDA: Automated Interactive Infrastructure and Database for Computational Science
This addresses the need for better tools in computational science, particularly materials science, to enhance collaboration and data management, though it is incremental as it applies existing computer science concepts to this domain.
The authors tackled the problem of managing, preserving, and sharing computational science efforts by introducing AiiDA, an open-source platform based on automation, data, environment, and sharing, which uses directed acyclic graphs for provenance tracking and enables reproducibility and workflow encoding.
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.