论文标题
在2D材料中探索和机器学习结构不稳定性
Exploring and machine learning structural instabilities in 2D materials
论文作者
论文摘要
我们解决了预测周期性晶体的零温度动力稳定性(DS)的问题,而无需计算其完整的声子结构。在这里,我们报告的证据表明,可以从Brillouin区(BZ)中心和边界的声子频率的可靠性中推断出DS。该分析代表了计算2D材料数据库(C2DB)采用的DS测试的验证。对于137个动态不稳定的2D晶体,我们沿着不稳定的模式将原子取代并放松结构。在49例情况下,该过程产生动态稳定的晶体。这些新结构的基本特性是使用C2DB工作流进行表征的,发现它们的性质与原始不稳定晶体的特性可以显着差异,例如频带差距平均开放0.3 eV。所有晶体结构和特性均在C2DB中可用。最后,我们使用编码晶体的电子结构的表示形式对C2DB中3295 2D材料的DS数据进行分类模型。我们获得了出色的接收器工作特性(ROC)曲线,曲线下的面积为0.90,表明分类模型可以大大减少高通量研究中的计算工作。
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.