Skip to main content

An implementation of DOS fingerprints for the NOMAD Laboratory.

Project description

This package implements fingerprints of the electronic density-of-states (DOS) for the evaluation of similarity of materials based on their electronic structures.

The fingerprints are based on a modification on the D-Fingerprints presented in Ref. [1]. Our modification allows to target specific energy ranges for the evaluation of the similarity of the electronic structure. As a similarity measure we use the Tanimoto coefficient [2].

Usage

Fingerprints are instances of the DOSFingerprint() class and can be calculated by providing the energy in [Joule] and the DOS in [states/unit cell/Joule] to the calculate() method. Furthermore, the parameters of a non-uniform grid can be chosen. The default grid is specialized on the energy range between -10 and 5 eV and emphasizes the upper valence region.

from nomad_dos_fingerprints import DOSFingerprint
dos_fingerprint = DOSFingerprint().calculate(<dos_energies>,<dos_values>)

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

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

References

[1] Isayev et al., Chem. Mater. 2015, 27, 3, 735–743 (doi:10.1021/cm503507h)

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nomad_dos_fingerprints-1.0.2.tar.gz (11.4 kB view details)

Uploaded Source

Built Distribution

nomad_dos_fingerprints-1.0.2-py3-none-any.whl (10.6 kB view details)

Uploaded Python 3

File details

Details for the file nomad_dos_fingerprints-1.0.2.tar.gz.

File metadata

File hashes

Hashes for nomad_dos_fingerprints-1.0.2.tar.gz
Algorithm Hash digest
SHA256 cc70624ee7f680d471e00fa31fccbc1c0bfec6410fa7f980dbaeaff2603249fe
MD5 cc92c5e8af2b9a789b4881b50ebebfeb
BLAKE2b-256 475d5f6c2d7ef225a92f326103d30a6333a4bc71030f344981d15b6e31b0ec5a

See more details on using hashes here.

File details

Details for the file nomad_dos_fingerprints-1.0.2-py3-none-any.whl.

File metadata

File hashes

Hashes for nomad_dos_fingerprints-1.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 cc01ed88d35f751ef13c89e75faeea16fef16ab8363b3a6fcb4f810afacb6338
MD5 47e86c4993f9655449e0bfe29db30c12
BLAKE2b-256 7f5df0604fa8e642562118ad330088959623ba84cb56c965a23b34c8e2ce225a

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page