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aiida-muon

An AiiDA workflow plugin for finding candidate muon implantation sites and computing the local magnetic field at those sites.

GitHub AiiDA

Positive muon spin rotation/relaxation/resonance (µSR) is a powerful experimental probe for studying magnetism, superconductivity, and other phenomena in condensed matter. A key step in interpreting µSR data is knowing where the muon stops inside the host material.

aiida-muon automates this search by running a battery of DFT supercell relaxations — via Quantum ESPRESSO through aiida-quantumespresso — and analysing the results with symmetry-based clustering. For magnetic materials it also computes the contact hyperfine field and the classical dipolar field at each candidate site.

  • Installation

    Install aiida-muon and its dependencies, then verify the setup.

    To the installation guide

  • Tutorials

    Step-by-step guides covering non-magnetic (Si), ferromagnetic (Fe), and antiferromagnetic (MnO) test cases.

    To the tutorials

  • How-To Guides

    Concise recipes for common tasks: building inputs, handling magnetic structures, DFT+U, pre-relaxation, and results export.

    To the how-to guides

  • Advanced Topics

    Workflow internals and the experimental machine-learning features.

    To the advanced topics

Key features

  • Automated generation of a grid of candidate muon stopping sites using the NICHE algorithm.
  • Full-mesh DFT relaxation of muon supercells via PwRelaxWorkChain (Quantum ESPRESSO).
  • Optional Gamma-point pre-relaxation to cheaply reduce the number of starting sites.
  • Optional MLIP pre-relaxation (experimental) for fast prescreening with machine-learning interatomic potentials.
  • Optional automated supercell size determination via aiida-impuritysupercellconv.
  • Symmetry-aware clustering of relaxed sites to identify unique candidate positions.
  • Contact hyperfine field from DFT spin density (pp.x) for magnetic systems.
  • Classical dipolar field computed with muesr.
  • Full AiiDA provenance: every intermediate result is stored in the database.

How to cite

If you use this package for published research, please cite:

Ifeanyi J. Onuorah, Miki Bonacci et al., Automated computational workflows for muon spin spectroscopy, Digital Discovery 4, 523-538 (2025).

Also cite the underlying AiiDA infrastructure:

Sebastiaan P. Huber et al., AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance, Scientific Data 7, 300 (2020).

Acknowledgements

We acknowledge support from: