Welcome to AiiDA’s documentation!

aiida-core version: 1.6.7

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 (see features).

Getting Started

AiiDA installation, configuration and troubleshooting.


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

How-To Guides

Learn how to use AiiDA to power your own work.


Background information on AiiDA’s underlying concepts.

API Reference

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

Internal Architecture

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

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:

AiiDA >= 1.0: 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

AiiDA >= 1.0: 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

AiiDA < 1.0: 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


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.