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A repository of examples, tutorials and demos to demonstrate AiiDA functionality
Jupyter Notebook Python Shell
Branch: master
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ltalirz remove tutorial folder (#21)
* Removing files, now moved to aiida-tutorials

See: aiidateam/aiida-tutorials#31

* bump aiidalab version (#20)

Update AiiDA lab version, fixing the binderhub build
Latest commit 618014b May 10, 2019
Type Name Latest commit message Commit time
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.local [WIP] binderize aiida demos (#10) Sep 18, 2018
common Use pseudo utility from aiida-quantumespresso plugin repository Mar 30, 2018
miscellaneous/logos Update metadata (#15) Mar 7, 2019
notebooks Update aiidalab-metapkg (#13) Dec 31, 2018
.gitignore Update metadata (#15) Mar 7, 2019
.travis.yml enable travis (#14) Dec 31, 2018
LICENSE Initial commit Apr 12, 2017 Acknowledge MARVEL and MaX Oct 11, 2018 Translated the equation of states workchain script into a notebook Apr 13, 2017
apt.txt [WIP] binderize aiida demos (#10) Sep 18, 2018
postBuild [WIP] binderize aiida demos (#10) Sep 18, 2018
requirements.txt remove tutorial folder (#21) May 10, 2019
runtime.txt [WIP] binderize aiida demos (#10) Sep 18, 2018
start [WIP] binderize aiida demos (#10) Sep 18, 2018 I had forgotten to comment the load_dbenv that is Apr 5, 2018



A repository of jupyter notebooks containing examples, tutorials and demos of AiiDA.


No installation needed - just click "launch binder"!

If you prefer to run the AiiDA demos on your computer, see the AiiDA documentation for instructions to install AiiDA.


pip install aiida-quantumespresso
git clone
cd aiida_demos/notebooks
jupyter notebook

A Jupyter tab should open in your browser from which you can select the notebook you want to run.

If Jupyter does not open automatically, point your browser to http://localhost:8888/tree/

Building the binder image locally (for development)

pip install jupyter-repo2docker
git clone
repo2docker aiida_demos

In order to enter directly into app-mode, use the URL<port>/apps/apps/home/start.ipynb


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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