aiida-wannierjl

aiida-wannierjl

An AiiDA plugin that wraps Wannier.jl (pinned to rev 65245c59 of the qiaojunfeng/Wannier.jl fork, requires Julia >= 1.11) to manipulate Wannier functions from within AiiDA workflows.

Each calculation renders a small Julia driver script (driver.jl) and runs it against a persistent, pinned Wannier.jl project environment. The AiiDA Code is the Julia binary itself, so the calculations are remote-capable and provenance is tracked like any other AiiDA CalcJob. Results come back through a machine-readable results.json that the parsers turn into AiiDA output nodes.

Features

The plugin provides three CalcJobs, one per Wannier.jl operation:

• wannierjl.check_neighbors (CheckNeighborsCalculation) — reads a wannier90 run (.win, .chk, .mmn, .amn, .eig) with read_w90_with_chk and reports whether the k-point stencil contains the six cubic nearest-neighbour b-vectors (Wannier.has_cubic_neighbors). Output: a Bool node has_cubic_neighbors.
• wannierjl.generate_neighbors (GenerateNeighborsCalculation) — writes a cubic.nnkp file with the six cubic b-vectors via Wannier.write_nnkp_cubic, so that a .mmn with cubic neighbours can be regenerated. Output: a SinglefileData node nnkp_file.
• wannierjl.split (SplitCalculation) — splits a Wannier manifold into blocks with Wannier.Tools.mrwf, given 1-based index groups. Outputs, under dynamic namespaces: per-block FolderData (blocks.block_i, containing amn/eig/mmn), the per-block .win files (win_files.block_i), and the per-block split U matrices (u_matrices.block_i, the <seedname>_split.amn files).

Input files for each CalcJob can be supplied either explicitly as SinglefileData ports or picked up from a parent calculation's remote working directory (parent_folders.* RemoteData), which is how the .chk file — not normally retrieved by upstream wannier90 plugins — is symlinked in from a wannier90 run on the same computer.

workflows extra

Installing the optional workflows extra pulls in aiida-workgraph and aiida-quantumespresso and exposes split_wannierization, an aiida-workgraph @task.graph that orchestrates the full split:

from aiida_wannierjl.workflows import split_wannierization

It runs check_neighbors and, only when the cubic neighbours are missing, generates the cubic.nnkp, regenerates the cubic .mmn with a Pw2wannier90Calculation (forcing write_mmn=.true., write_amn=.false., SCDM off), and then runs split — all in a single invocation, with the cubic branch decided at runtime. Re-wannierization and U-matrix merging are deliberately out of scope and stay in the downstream consumer (koopmans).

Julia environment

The Wannier.jl project environment is created once per machine, never per calculation. A helper builds the pinned project (Pkg.add of Wannier.jl at rev 65245c59 plus JSON) and, by default, a PackageCompiler.jl sysimage so that each calculation loads in ~0.1 s instead of paying the multi-second using Wannier cost on every fresh process. The sysimage path travels on the Code node, and the CalcJobs pick it up automatically (falling back to a plain --project load when no sysimage is known).

See the get started guide for the full setup procedure (setup_julia_environment and get_wannierjl_code in aiida_wannierjl.helpers).

Installation

pip install aiida-wannierjl[workflows]   # drop [workflows] if you don't need the workgraph
verdi quicksetup                         # better to set up a new profile
verdi plugin list aiida.calculations     # should list the three wannierjl.* plugins

You also need a Julia >= 1.11 installation and the one-time environment setup described above.

Usage

Once you have registered a Julia code (labelled e.g. wannierjl@localhost), a minimal generate_neighbors run looks like:

verdi daemon start     # make sure the daemon is running
cd examples
./example_01.py        # generate a cubic.nnkp from a small .win
verdi process list -a  # check the record of the calculation

See examples/example_01.py for the corresponding Python.

Development

git clone https://github.com/elinscott/aiida-wannierjl .
cd aiida-wannierjl
pip install --upgrade pip
pip install -e .[pre-commit,workflows]  # install extra dependencies
pre-commit install                      # install pre-commit hooks
pytest -v                               # discover and run all tests

The test suite mocks the Julia code, so it runs in CI without a real Julia installation. See the developer guide for more information.

Citing

If you use this plugin for your research, please cite the following work:

• J. Qiao, G. Pizzi, and N. Marzari, Automated mixing of maximally localized Wannier functions into target manifolds, npj Comput. Mater. 9, 206 (2023), https://doi.org/10.1038/s41524-023-01147-9.
• J. Qiao, G. Pizzi, and N. Marzari, Projectability disentanglement for accurate and automated electronic-structure Hamiltonians, npj Comput. Mater. 9, 208 (2023), https://doi.org/10.1038/s41524-023-01146-w.

See CITATION.cff (or the GitHub "Cite this repository" button) for how to cite this plugin itself.

License

MIT

Contact

edwardlinscott@gmail.com