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Installation

Prerequisites

Before installing aiida-muon, make sure you have:

  • Python 3.9 or later
  • A working AiiDA installation (≥ 2.0) with a configured profile
  • A configured Quantum ESPRESSO pw.x code in AiiDA (for DFT calculations)

If AiiDA is not yet set up on your system, follow the AiiDA core installation guide first.

Install from source

git clone https://github.com/positivemuon/aiida-muon.git
cd aiida-muon
pip install -e .

This installs the core package together with its required dependencies:

Dependency Purpose
aiida-core >=2.0 AiiDA engine and ORM
aiida-quantumespresso >=4.2 QE plugin (PwRelaxWorkChain, etc.)
aiida-impuritysupercellconv Automated supercell size convergence
aiida-qe-restart Robust QE restart handling
aiida-monitor Optional job monitoring
pymatgen Structure manipulation and symmetry analysis
muesr / muLFC Dipolar field computation

Quantum ESPRESSO version

aiida-muon requires Quantum ESPRESSO ≥ 7.1 because it depends on the updated Hubbard input-card format introduced in that version.

Optional: machine-learning features

The experimental MLIP pre-relaxation and active-learning modules require additional packages not installed by default:

# For MLIP pre-relaxation via aiida-pythonjob
pip install git+https://github.com/mikibonacci/aiida-pythonjob@fix_serializer
pip install numpy==2

# Choose one or more MLIP backends
pip install mace-torch       # MACE
pip install chgnet            # CHGNet
pip install mattersim         # MatterSim

Machine-learning features are experimental

The MLIP pre-relaxation (ML_pre_relax) and the active-learning workflow (ActiveLearningWorkChain) are experimental and subject to change without notice. Expect rough edges and consult the advanced topics for details before using them in production runs.

Install pseudopotential families

The workflow requires a pseudopotential family. The default is SSSP/1.3/PBE/efficiency:

aiida-pseudo install sssp -v 1.3 -x PBE -p efficiency

Verify the installation

from aiida import load_profile
load_profile()

from aiida.plugins import WorkflowFactory
FindMuonWorkChain = WorkflowFactory('muon.find_muon')
print(FindMuonWorkChain)
# <class 'aiida_muon.workflows.find_muon.FindMuonWorkChain'>

You can also list all registered entry points:

verdi plugin list aiida.workflows muon

Expected output: 

muon.find_muon     aiida_muon.workflows.find_muon:FindMuonWorkChain
muon.fine_tuning   aiida_muon.workflows.finetuning:FineTuningWorkChain
muon.active_learning  aiida_muon.workflows.active_learning:ActiveLearningWorkChain