MLINDEX models
Machine-learning models used by MLINDEX, a powder diffraction indexing program. Given a list of observed diffraction peaks, MLINDEX returns candidate unit cells ranked by the de Wolff M20 figure of merit. These models initialize the candidate unit cells for each Bravais lattice; the candidates are then refined by least-squares optimization.
Contents
One directory per lattice system, each holding the trained components for its Bravais lattices and split groups:
| Directory | Bravais lattices |
|---|---|
cubic_1/ |
cF, cI, cP |
hexagonal_1/ |
hP |
rhombohedral_1/ |
hR |
tetragonal_1/ |
tI, tP |
orthorhombic_1/ |
oC, oF, oI, oP |
monoclinic_1/ |
mC, mP |
triclinic_1/ |
aP |
Within each, random_forest/ and random/ hold random-forest volume predictors,
template/ holds the Miller-index template libraries and their calibrators,
integral_filter/ holds the quantized ONNX candidate-filter networks, and data/ holds the
hkl_ref_*.npy reference sets and training parameters.
Total: 780 files, ~545 MB.
Usage
pip install mlindex
mlindex.download_models
mlindex.download_models fetches this repository at the revision pinned by your installed
mlindex version. No git or git-lfs required.
To fetch it directly:
from huggingface_hub import snapshot_download
snapshot_download("dwmoreau/mlindex-models", revision="v1", local_dir="models")
Revisions
Releases of mlindex pin a specific tag, so a given version always gets the exact weights it
was tested against. v1 corresponds to the models released with mlindex 0.1.x.
Citation
Please check the GitHub repository for the current citation.
License
MIT, matching the MLINDEX source.