Exploring and machine learning structural instabilities in 2D materials

TL;DR

This paper develops a machine learning approach to predict the dynamical stability of 2D materials efficiently, validated by structural relaxations and property analysis, significantly reducing computational costs in materials discovery.

Contribution

The study introduces a reliable phonon-based stability test validation and a high-accuracy classification model for large-scale 2D materials stability prediction.

Findings

Phonon frequencies at BZ center and boundary reliably predict stability.

Displacing unstable modes can stabilize 49 out of 137 unstable crystals.

The classification model achieves an AUC of 0.90, enabling efficient high-throughput screening.

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 characterised 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.