scikit-matterΒΆ

scikit-matter is a toolbox of methods developed in the computational chemical and materials science community, following the scikit-learn API and coding guidelines to promote usability and interoperability with existing workflows. You can get the latest version from its github repository and learn how to use it from its documentation.

Sample and Feature Selection with FPS and CUR
In this tutorial we generate descriptors using rascaline, then select a subset of structures using both the farthest-point sampling (FPS) and CUR algorithms implemented in scikit-matter. Finally, we also generate a selection of the most important features using the same techniques.
Sample and Feature Selection with FPS and CUR
Generalized Convex Hull construction for the polymorphs of ROY
This notebook analyzes the structures of 264 polymorphs of ROY, from Beran et Al, Chemical Science (2022), comparing the conventional density-energy convex hull with a Generalized Convex Hull (GCH) analysis (see Anelli et al., Phys. Rev. Materials (2018)). It uses features computed with rascaline and uses the directional convex hull function from scikit-matter to make the figure.
Generalized Convex Hull construction for the polymorphs of ROY
LPR analysis for amorphous silicon dataset
In this tutorial, we calculate the SOAP descriptors of an amorphous silicon dataset using rascaline, then compute the local prediction rigidity (LPR) for the atoms of a "test" set before and after modifications to the "training" dataset has been made.
LPR analysis for amorphous silicon dataset
PCA/PCovR Visualization for the rattled GaAs training dataset
This example uses rascaline and metatensor to compute structural properties for the structures in a training for a ML model. These are then used with simple dimensionality reduction algorithms (implemented in sklearn and skmatter) to obtain a simplified description of the dataset, that is then visualized using chemiscope.
PCA/PCovR Visualization for the rattled GaAs training dataset