aiida-core version: 1.1.0

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Fig. 1 Automated Interactive Infrastructure and Database for Computational Science

Welcome to AiiDA’s documentation!

AiiDA is a python framework that aims to help researchers with managing complex workflows and making them fully reproducible.

Features

  • Workflows: Write complex, auto-documenting workflows in python, linked to arbitrary executables on local and remote computers. The event-based workflow engine supports tens of thousands of processes per hour with full checkpointing.

  • Data provenance: Automatically track inputs, outpus & metadata of all calculations in a provenance graph for full reproducibility. Perform fast queries on graphs containing millions of nodes.

  • HPC interface: Move your calculations to a different computer by changing one line of code. AiiDA is compatible with schedulers like SLURM, PBS Pro, torque, SGE or LSF out of the box.

  • Plugin interface: Extend AiiDA with plugins for new simulation codes (input generation & parsing), data types, schedulers, transport modes and more.

  • Open Science: Export subsets of your provenance graph and share them with peers or make them available online for everyone on the Materials Cloud.

  • Open source: AiiDA is released under the MIT open source license.

See also the AiiDA home page.

Getting started

For developers

  • If you wish to start developing a plugin for AiiDA the Basics section is a good place to start.

  • Practical information concerning how to set up your development environment and how to contribute to aiida core can be found on the AiiDA Wiki.

  • You are also more than welcome to join the AiiDA mailing list or contribute to aiida-core on GitHub.

How to cite

If you use AiiDA for your research, please cite the following work:

Giovanni Pizzi, Andrea Cepellotti, Riccardo Sabatini, Nicola Marzari, and Boris Kozinsky, AiiDA: automated interactive infrastructure and database for computational science, Comp. Mat. Sci 111, 218-230 (2016); https://doi.org/10.1016/j.commatsci.2015.09.013; http://www.aiida.net.

Acknowledgements

This work is supported by: * the MARVEL National Centre for Competency in Research funded by the Swiss National Science Foundation; * the MaX European Centre of Excellence funded by the Horizon 2020 EINFRA-5 program, Grant No. 676598; * the swissuniversities P-5 project “Materials Cloud”.

AiiDA is a NumFOCUS Affiliated Project. Visit numfocus.org for more information.

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