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Project description
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 
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.
How to cite
If you use AiiDA in your research, please consider citing the AiiDA paper:
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.
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
This work is supported by the MARVEL National Centre for Competency in Research funded by the Swiss National Science Foundation, as well as by the MaX European Centre of Excellence funded by the Horizon 2020 EINFRA-5 program, Grant No. 676598.
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