Exploring and machine learning structural instabilities in 2D materials

作     者：Simone Manti Mark Kamper Svendsen Nikolaj R.Knøsgaard Peder M.Lyngby Kristian S.Thygesen 

作者机构：CAMDComputational Atomic-Scale Materials DesignDepartment of PhysicsTechnical University of Denmark2800 Kgs.LyngbyDenmark Center for Nanostructured Graphene(CNG)Department of PhysicsTechnical University of DenmarkDK-2800 Kongens LyngbyDenmark 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2023年第9卷第1期

页      面：2016-2025页

核心收录：

学科分类：08[工学] 0703[理学-化学] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The Center for Nanostructured Graphene(CNG)is sponsored by the Danish National Research Foundation,Project DNRF103 This project has received funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation program grant agreement no.773122(LIMA) K.S.T.is a Villum Investigator supported by VILLUM FONDEN(grant no.37789) 

主　　题：structure unstable structural 

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