论文标题
使用Magus预测表面重建
Prediction of surface reconstructions using MAGUS
论文作者
论文摘要
在本文中，我们提出了一个新的模块，以预测机器学习和图理论辅助通用结构搜索器（MAGUS）框架中给定表面结构的潜在表面重建配置。除了用特定晶格对称性产生的随机结构外，我们还充分利用了大量材料来获得更好的种群能量分布，即，随机粘附于从散装结构切割的表面或移动/移动/去除表面上的某些原子，这是由自然表面构造过程启发的。此外，考虑到不同原子数的表面模型通常具有一些共同的基础，我们从集群预测中借了想法来更好地传播结构。为了验证这个新开发的模块，我们分别对SI（100），SI（111）和4H-SIC（1-102）-C（2x2）的表面重建进行了研究。我们成功地在非常富裕的环境中成功地提供了已知的基础状态以及新的SIC表面模型。
In this paper, we present a new module to predict the potential surface reconstruction configurations of given surface structures in the framework of our machine learning and graph theory assisted universal structure searcher (MAGUS). In addition to random structures generated with specific lattice symmetry, we made full use of bulk materials to obtain a better distribution of population energy, namely, randomly appending atoms to a surface cleaved from bulk structures or moving/removing some of the atoms on the surface, which is inspired by natural surface reconstruction processes. In addition, we borrowed ideas from cluster predictions to spread structures better between different compositions, considering that surface models of different atom numbers usually have some building blocks in common. To validate this newly developed module, we tested it with studies on the surface reconstructions of Si (100), Si (111) and 4H-SiC(1-102)-c(2x2), respectively. We successfully gave the known ground states as well as a new SiC surface model in an extremely Si-rich environment.
