rascaline's Introduction

Rascaline

Rascaline is a library for the efficient computing of representations for atomistic machine learning also called "descriptors" or "fingerprints". These representations can be used for atomistic machine learning (ml) models including ml potentials, visualization or similarity analysis.

The core of the library is written in Rust and we provide APIs for C/C++ and Python as well.

Warning

Rascaline is still as the proof of concept stage. You should not use it for anything important.

List of implemented representations

representation	description	gradients
Spherical expansion	Atoms are represented by the expansion of their neighbor's density on radial basis and spherical harmonics. This is the core of representations in SOAP (Smooth Overlap of Atomic Positions)	positions and cell
SOAP radial spectrum	Atoms are represented by 2-body correlations of their neighbors' density	positions and cell
SOAP power spectrum	Atoms are represented by 3-body correlations of their neighbors' density	positions and cell
LODE Spherical Expansion	Core of representations in LODE (Long distance equivariant)	positions
Sorted distances	Each atomic center is represented by a vector of distance to its neighbors within the spherical cutoff	no
Neighbor List	Each pair is represented by the vector between the atoms. This is intended to be used as a starting point for more complex representations	positions
AtomicComposition	Obtaining the stoichiometric information of a structure	positions and cell