AiiDA

AiiDA (www.aiida.net) is a workflow manager for computational science with a strong focus on provenance, performance and extensibility.
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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, outputs & 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

Installation

Please see AiiDA's documentation.

How to contribute PRs Welcome GitHub issues by-label

The AiiDA team appreciates help from a wide range of different backgrounds. Small improvements of the documentation or minor bug fixes are always welcome.

Please see the Contributor wiki on how to get started.

Frequently Asked Questions

If you are experiencing problems with your AiiDA installation, please refer to the FAQ page of the documentation. For any other questions, discussion and requests for support, please visit the Discourse server.

How to cite

If you use AiiDA in your research, please consider citing the following publications:

  • S. P. Huber et al., AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance, Scientific Data 7, 300 (2020); DOI: 10.1038/s41597-020-00638-4
  • M. Uhrin et al., Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows, Computational Materials Science 187, 110086 (2021); DOI: 10.1016/j.commatsci.2020.110086

If the ADES concepts are referenced, please also cite:

  • Giovanni Pizzi, Andrea Cepellotti, Riccardo Sabatini, Nicola Marzari,and Boris Kozinsky, AiiDA: automated interactive infrastructure and database for computational science, Computational Materials Science 111, 218-230 (2016); DOI: 10.1016/j.commatsci.2015.09.013

License

AiiDA is distributed under the MIT open source license (see LICENSE.txt). For a list of other open source components included in AiiDA, see open_source_licenses.txt.

Acknowledgements

AiiDA is a NumFOCUS Affiliated Project and supported by the MARVEL National Centre of Competence in Research, the MaX European Centre of Excellence and by a number of other supporting projects, partners and institutions, whose complete list is available on the AiiDA website acknowledgements page.

Project links

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  • Released:
Newer release available (2.9.2)

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Credits

Author: The AiiDA team

GitHub Statistics

Data verified by PyPI on Aug 27, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License

Requires

Python >=3.10

Provides Extra

atomic-tools bpython docs notebook pre-commit rest ssh-kerberos tests tui

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aiida workflows

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