mlindex-models

dwmoreau/mlindex-models

mlindex-models is a machine learning model from dwmoreau. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlindex. The card lists the license as mit.

Downloads · 30 days

Access

Public

Updated Sep 13, 2026

Repo size

569 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.npz220 MB · 47%

At a glance

Library: mlindex
License: mit
Access: Public
Created: Aug 14, 2026
Updated: Sep 13, 2026
SHA: 179980f6

Model card

From the Hugging Face model README

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.

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, abnn/ holds the quantized ONNX ABNN networks (an attention-based network that predicts unit cells from the peak list), and data/ holds the hkl_ref_*.npy reference sets and training parameters.

Total: 739 files, 465 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="v2", local_dir="models")

Revisions

Releases of mlindex pin a specific tag, so a given version always gets the exact weights it was tested against.

v2 removes the 43 per-peak Miller-index assignment networks (104.7 MB), which a closed-form posterior replaced, and renames integral_filter/ to abnn/. It needs an mlindex that looks for abnn/.
v1 corresponds to the models released with mlindex 0.1.x, which keep working against it.