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

Abstract

Funding Information: We acknowledge the funding from the European Union’s Horizon program under Grant Agreement No. 951786, the Academy of Finland through Project No. 334532, and the Center of Excellence Virtual Laboratory for Molecular Level Atmospheric Transformations (VILMA; Project No. 346377). We further acknowledge the CSC-IT Center for Science, Finland, and the Aalto Science-IT project. | openaire: EC/H2020/951786/EU//NOMAD CoE ; 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. ; Peer reviewed

Topics

• perovskite
• cluster
• machine learning