What is AiiDA?
AiiDA is an open-source Python infrastructure to help researchers with automating, managing, persisting, sharing and reproducing the complex workflows associated with modern computational science and all associated data.
AiiDA is built to support and streamline the four core pillars of the ADES model: Automation, Data, Environment, and Sharing. Key features include:
- Workflows: AiiDA allows to build and execute complex, auto-documenting workflows linked to multiple codes on local and remote computers.
- High-throughput: AiiDA’s event-based workflow engine supports tens of thousands of processes per hour with full check-pointing.
- Data provenance: AiiDA automatically tracks and records inputs, outputs and metadata of all calculations and workflows in extensive provenance graphs that preserve the full lineage of all data.
- Advanced queries: AiiDA’s query language enables fast graph queries on millions of nodes.
- Plugin interface: AiiDA can support via plugins any computational code and data analytics tool, data type, scheduler, connection mode, etc. (see public plugin repository)
- HPC interface: AiiDA can seamlessly deal with heterogeneous and remote computing resources; it works with many schedulers out of the box (SLURM, PBS Pro, torque, SGE or LSF).
- Open science: AiiDA allows to export both full databases and selected subsets, to be shared with collaborators or made available and browsable online on the Archive and Explore sections of Materials Cloud.
- Open source: AiiDA is released under the MIT open-source license.
Most recent news
The AiiDA team are pleased to announce that registration for our upcoming virtual tutorial (July...
There are three new openings at EPFL in the group of Prof. Marzari for a...
After a successful Google Summer of Code (GSOC) 2020 under the NumFocus umbrella, the AiiDA...
We are happy to inform you that we have just released v1.6.0!As per SemVer versioning,...
The paper providing comprehensive information on the architecture and design philosophy of the AiiDAlab web...
The paper describing the details of AiiDA's workflow engine has been published today: M. Uhrin...