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)