Updates to the DScribe library : New descriptors and derivatives

Jarno Laakso*, Lauri Himanen, Henrietta Homm, Eiaki V. Morooka, Marc O.J. Jäger, Milica Todorović, Patrick Rinke

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)


We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe’s descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DScribe. For the many-body tensor representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), we have also implemented analytic derivatives. We demonstrate the effectiveness of the descriptor derivatives for machine learning models of Cu clusters and perovskite alloys.

Original languageEnglish
Article number234802
Pages (from-to)1-8
Number of pages8
JournalJournal of Chemical Physics
Issue number23
Publication statusPublished - 21 Jun 2023
MoE publication typeA1 Journal article-refereed


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