Analysis and post-processing¶

This section contains recipes that analyze the output of a simulation, for analysis or visualization purposes.

PCA/PCovR Visualization of a training dataset for a potential

This example uses rascaline and metatensor to compute structural properties for the structures in a training dataset for a ML potential. 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 of a training dataset for a potential

Local Prediction Rigidity analysis

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.

Local Prediction Rigidity analysis

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