Advanced AiiDA workflows for Wannier90

The aiida-wannier90-workflows package is an AiiDA plugin for running advanced workflows with the wannier90 software suite.

Installation

You can install aiida-wannier90-workflows in your Python environment using pip:

$ pip install aiida-wannier90-workflows

Note that this command will also install the aiida-core, aiida-quantumespresso, aiida-wannier90 packages as its dependencies. For more information on how to install AiiDA and the required services in different environments, we refer to the aiida-core documentation.

Compatibility

For an overview of the plugin’s compatibility with Python, AiiDA and wannier90, please refer to the README.md of the repository.

Getting Started

A good place to get started with running Quantum ESPRESSO calculations using AiiDA is the AiiDA Quantum ESPRESSO tutorial on the main AiiDA tutorials page. You can either run the tutorial in your own installation if completed, or use the resources mentioned there, i.e. the Quantum Mobile virtual machine.

Warning

The documentation is currently undergoing a major revision, but likely still contains some outdated content. If you find any material that you think is incorrect, please open an issue on the GitHub repository.

Contents

• User’s guide
  • Get started
• API reference
  • Workflows
  • Utils
• CLI reference
  • node
  • list
  • plot
  • group
  • statistics

Indices and tables

• Index

• Module Index

• Search Page

Acknowledgements

If you use this plugin and/or 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)

• 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)

We acknowledge support from:

The NCCR MARVEL funded by the Swiss National Science Foundation.
The EU Centre of Excellence “MaX – Materials Design at the Exascale” (Horizon 2020 EINFRA-5, Grant No. 676598).
The swissuniversities P-5 project “Materials Cloud”.