Exploring and machine learning structural instabilities in 2D materials

• Published in: npj computational materials 2023-03, Vol.9 (1), p.33-10, Article 33
• Main Authors: Manti, Simone, Svendsen, Mark Kamper, Knøsgaard, Nikolaj R., Lyngby, Peder M., Thygesen, Kristian S.
• Format: Article
• Language: English
• Subjects: 639/766/119/1000/1018; 639/766/119/995; Brillouin zones; Characterization and Evaluation of Materials; Chemistry and Materials Science; Classification; Computational Intelligence; Computer applications; Crystal structure; Crystals; Dynamic stability; Eigenvalues; Electronic structure; Machine learning; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Phase transitions; Phonons; Physics; Structural stability; Symmetry; Theoretical; Two dimensional materials; Workflow
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-023-00977-x

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.