# rascaline¶

Rascaline is a library for the efficient computing of representations for atomistic machine learning also called “descriptors” or “fingerprints”. These representation can be used for atomistic machine learning (ML) models including ML potentials, visualization or similarity analysis. You can learn how to use it reading the documentation, and get the latest version from the github repository

PCA/PCovR Visualization of a training dataset for a potential

Local Prediction Rigidity analysis

A ML model for the electron density of states

Long-distance Equivariants: a tutorial

Generalized Convex Hull construction for the polymorphs of ROY

Sample and Feature Selection with FPS and CUR

Periodic Hamiltonian learning