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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.

Citing featomic

If you found featomic useful for your work, please cite the corresponding article:

F. Bigi, J.W. Abbott, P. Loche et al., Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning, (2026). https://doi.org/10.1063/5.0304911

@article{bigi_metatensor_2026,
  title = {Metatensor and Metatomic: {{Foundational}} Libraries for Interoperable Atomistic Machine Learning},
  shorttitle = {Metatensor and Metatomic},
  author = {Bigi, Filippo and Abbott, Joseph W. and Loche, Philip and Mazitov, Arslan and Tisi, Davide and Langer, Marcel F. and Goscinski, Alexander and Pegolo, Paolo and Chong, Sanggyu and Goswami, Rohit and Febrer, Pol and Chorna, Sofiia and Kellner, Matthias and Ceriotti, Michele and Fraux, Guillaume},
  year = 2026,
  month = feb,
  journal = {J. Chem. Phys.},
  volume = {164},
  number = {6},
  pages = {064113},
  issn = {0021-9606},
  doi = {10.1063/5.0304911},
}

Release files for featomic 0.6.7

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for featomic 0.6.7
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Built distributions (wheels)

Table of built distributions (wheels) for featomic 0.6.7
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featomic-0.6.7-py3-none-win_amd64.whl Python 3 none Windows x86-64 Details
featomic-0.6.7-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl Python 3 none Linux glibc 2.17+ x86-64 Details
featomic-0.6.7-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl Python 3 none Linux glibc 2.17+ ARM64 Details
featomic-0.6.7-py3-none-macosx_11_0_x86_64.whl Python 3 none macOS 11.0+ x86-64 Details
featomic-0.6.7-py3-none-macosx_11_0_arm64.whl Python 3 none macOS 11.0+ ARM64 Details

Total release size: 6.0 MB

Release files / featomic-0.6.7.tar.gz

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