AiiDA: Automated Interactive Infrastructure and Database for Computational Science

By Giovanni Pizzi, Andrea Cepellotti, R. Sabatini, Nicola Marzari, Boris Kozinsky. arXiv.org, 2015

571 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Information Systems and Management, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/a70bc74d4522f8af492a09a3ea9235c4e1e2fdbf

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In Materials Chemistry

Citations in the corpus

Materials Cloud, a platform for open computational science (2020, methodology): “Materials Cloud is powered by AiiDA, a workflow manager for computational science with a strong focus on provenance, performance and extensibility [16, 17].”
A universal graph deep learning interatomic potential for the periodic table (2022, background): “with weights wi summing to 1 and defined as wi G ξ3Gvi) ∑ i ξ3Gvi) (18)”
Accelerating the discovery of materials for clean energy in the era of smart automation (2018, unknown): “Overall, orchestration of the aforementioned tools, at both the hardware and software level, makes the construction of workflows (for example, Awesome Pipeline) challenging 183 .”