Welcome to AiiDA’s documentation!#

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AiiDA

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 (see features).

aiida-core version: 2.4.0.post0

Getting Started

AiiDA installation, configuration and troubleshooting.

To the installation guides

Tutorial

First time users: Get your feet wet with AiiDA basics!

To the tutorials

How-To Guides

Learn how to use AiiDA to power your own work.

To the how-to guides

Topics

Background information on AiiDA’s underlying concepts.

To the topics

API Reference

Comprehensive documentation of AiiDA components: command-line interface, Python interface, and RESTful API.

To the reference guide

Internal Architecture

Notes on AiiDA’s design and architecture aimed at core developers.

To the architecture guide

Need support?

For any questions, discussions and requests for support, please visit the Discourse forum.

Development Contributions

Saw a typo in the documentation? Want to improve the code? Help is always welcome, get started with the contributing guidelines.

How to cite#

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

Sebastiaan. P. Huber, Spyros Zoupanos, Martin Uhrin, Leopold Talirz, Leonid Kahle, Rico Häuselmann, Dominik Gresch, Tiziano Müller, Aliaksandr V. Yakutovich, Casper W. Andersen, Francisco F. Ramirez, Carl S. Adorf, Fernando Gargiulo, Snehal Kumbhar, Elsa Passaro, Conrad Johnston, Andrius Merkys, Andrea Cepellotti, Nicolas Mounet, Nicola Marzari, Boris Kozinsky, and Giovanni Pizzi, 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

Martin Uhrin, Sebastiaan. P. Huber, Jusong Yu, Nicola Marzari, and Giovanni Pizzi, 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:

Martin Uhrin, Sebastiaan. P. Huber, Jusong Yu, Nicola Marzari, and Giovanni Pizzi, 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 <https://doi.org/10.1016/j.commatsci.2020.110086>

Acknowledgements#

AiiDA is 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.

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