| --- |
| 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 |
|
|
