---
license: bsd-3-clause
language:
  - en
tags:
  - atomistic-machine-learning
  - materials-science
---

# ecumetric

This repository contains the ecumetric model and sketch-map projector which were used in
MAD-CORE.

The ecumetric model (`ecumetric_model.pt`) provides descriptors used to construct the
MAD-CORE hierarchy. The model uses the SPACE architecture of
[metatrain](https://github.com/metatensor/metatrain). Its per-atom `feature` output is
pooled into one 512-dimensional vector per structure, with concatenation of the mean and
the standard deviation over atoms. Distances between these vectors, after scaling,
define the ecumetric metric.

The ecumetric explorer model (`ecumetric_explorer.pt`) maps the ecumetric descriptors to
eight-dimensional sketch-map coordinates for exploration and visualization.

For re-exporting, see [reproduce/README.md](reproduce/README.md).

## Exploring a dataset with chemiscope

```python
import ase.io
import chemiscope

structures = ase.io.read("structures.xyz", ":")
chemiscope.explore(structures, featurizer="ecumetric-1.0")
```

## Citation

- MAD-CORE: Sofiia Chorna, Cesare Malosso, Jonathan Schmidt, Michele Ceriotti,
  *Hierarchical universal open structure dataset for atomistic modeling*,
  https://arxiv.org/abs/2610.21771
- SPACE architecture: https://arxiv.org/abs/2610.10830
- sketch-map: https://doi.org/10.1073/pnas.1108486108