featomic 0.6.3

Computing representations for atomistic machine learning

Featomic 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.

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, strain, cell
SOAP radial spectrum	Atoms are represented by 2-body correlations of their neighbors’ density	positions, strain, cell
SOAP power spectrum	Atoms are represented by 3-body correlations of their neighbors’ density	positions, strain, cell
LODE Spherical Expansion	Core of representations in LODE (Long distance equivariant)	positions
Sorted distances	Each atom 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 system	positions, strain, cell

For details, tutorials, and examples, please have a look at our documentation.