Exploring and machine learning structural instabilities in 2D materials

Simone Manti*, Mark Kamper Svendsen, Nikolaj R. Knøsgaard, Peder M. Lyngby, Kristian S. Thygesen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

31 Downloads (Pure)

Abstract

We address the problem of predicting the zero-temperature dynamical stability (DS) of a periodic crystal without computing its full phonon band structure. Here we report the evidence that DS can be inferred with good reliability from the phonon frequencies at the center and boundary of the Brillouin zone (BZ). This analysis represents a validation of the DS test employed by the Computational 2D Materials Database (C2DB). For 137 dynamically unstable 2D crystals, we displace the atoms along an unstable mode and relax the structure. This procedure yields a dynamically stable crystal in 49 cases. The elementary properties of these new structures are characterized using the C2DB workflow, and it is found that their properties can differ significantly from those of the original unstable crystals, e.g., band gaps are opened by 0.3 eV on average. All the crystal structures and properties are available in the C2DB. Finally, we train a classification model on the DS data for 3295 2D materials in the C2DB using a representation encoding the electronic structure of the crystal. We obtain an excellent receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.90, showing that the classification model can drastically reduce computational efforts in high-throughput studies.

Original languageEnglish
Article number33
Journalnpj Computational Materials
Volume9
Issue number1
Number of pages10
ISSN2057-3960
DOIs
Publication statusPublished - 2023

Fingerprint

Dive into the research topics of 'Exploring and machine learning structural instabilities in 2D materials'. Together they form a unique fingerprint.

Cite this