spectral-fingerprints 2.1.1

Binary-valued fingerprints for describing spectral properties.

Fingerprinting of materials based on their electronic structure.

Python package to compute binary valued fingerprints of spectral quantities and evaluate their similarity.

This package implements the electronic density-of-states fingerprints and the similarity metrics introduced in Refs. [1,2].

As a similarity measure we use the Tanimoto coefficient [3].

This package is the continuation of https://github.com/kubanmar/dos-fingerprints.

Installation

The package can be installed via pip:

pip install spectral-fingerprints

or you can clone and install from source:

git clone https://github.com/kubanmar/spectral-fingerprints.git
cd spectral-fingerprints
pip install .

Usage

Fingerprints of, e.g., the electronic density-of-states (DOS), are instances of the SpectralFingerprint() class and can be calculated by providing, e.g., the energy in [eV] and the DOS in [states/unit cell/eV] to the calculate() method. Furthermore, the energy axis can be discretized over a non-uniform grid. For this, specific parameters must be provided. By default, the grid is specialized on the energy range between -10 and 5 eV, thereby emphasizing the upper valence region.

from spectral_fingerprints import SpectralFingerprint
dos_fingerprint = SpectralFingerprint().calculate(<dos_energies>,<dos_values>)

To evaluate the similarity, the function tanimoto_similarity() can be used:

from spectral_fingerprints import tanimoto_similarity
tc = tanimoto_similarity(dos_fingerprint_1, dos_fingerprint_2)

Additionally, the SpectralFingerprint() functions get_similarty and get_similarities can be used:

similarity = dos_fingerprint_1.get_similarity(dos_fingerprint_2)
similarities = dos_fingerprint_1.get_similarity([dos_fingerprint_1, dos_fingerprint_2])

Citation

If you use this package in a publication, please cite it in the following way:

Martin Kuban, Santiago Rigamonti, Markus Scheidgen, and Claudia Draxl: Density-of-states similarity descriptor for unsupervised learning from materials data Sci Data 9, 646 (2022). https://doi.org/10.1038/s41597-022-01754-z

References

[1] Martin Kuban, Santiago Rigamonti, Markus Scheidgen, and Claudia Draxl: Density-of-states similarity descriptor for unsupervised learning from materials data. Sci Data 9, 646 (2022). https://doi.org/10.1038/s41597-022-01754-z

[2] Martin Kuban, Šimon Gabaj, Wahib Aggoune, Cecilia Vona, Santiago Rigamonti, Claudia Draxl: Similarity of materials and data-quality assessment by fingerprinting. MRS Bulletin 47, 991–999 (2022). https://doi.org/10.1557/s43577-022-00339-w

[3] P. Willet et al., J. Chem. Inf. Comput. 38 , 983 996 (1998) (https://doi.org/10.1021/ci9800211)