Atomic structures of grain boundaries for Si and Ge: A simulated annealing method with artificial-neural-network interatomic potentials
Atomic structures of grain boundaries for Si and Ge: A simulated annealing method with artificial-neural-network interatomic potentials
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Si 和 Ge 晶界的原子结构：采用人工神经网络原子间势的模拟退火方法
DOI：
10.1016/j.jpcs.2022.111114
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发表时间：
2023
影响因子：
4
通讯作者：
K. Matsunaga
中科院分区：
文献类型：
--
作者：
T. Yokoi;Y. Oshima;K. Matsunaga
为了准确预测Si和Ge中对称倾斜晶界（GBs）的低能结构，构建了人工神经网络（ANN）原子间势，并结合基于分子动力学模拟的模拟退火（SA）方法。人工神经网络驱动的SA方法被证明可以预测与先前电子显微镜观察结果非常一致的GB结构，而无需事先了解其原子构型。它们的GB能量也符合密度泛函理论（DFT）的计算结果。相比之下，传统的经验势无法预测这些国标结构。当取向角2 θ≥93.37°时，发现最低能结构包含的原子构型不能被完美晶体沿倾斜轴的一个重复单元再现。这种GB结构不能用γ-表面法获得，尽管它最常用于探索低能GB结构。这些结果强调了沿倾斜轴使用具有多个重复单元的模拟细胞以及使用可转移到gb的高精度原子间电位进行SA方法的重要性。
To accurately predict low-energy structures for symmetric tilt grain boundaries (GBs) in Si and Ge, artificial-neural-network (ANN) interatomic potentials are constructed and are combined with a simulated annealing (SA) method based on molecular dynamics simulations. The ANN-driven SA method is demonstrated to predict GB structures that are in good agreement with previous electron microscopy observations, without prior knowledge about their atomic configurations. Their GB energies also reasonably agree with density-functional-theory (DFT) calculations. By contrast, a conventional empirical potential fails to predict those GB structures. For misorientation angles 2 θ≥ 93.37°, the lowest-energy structures are found to contain atomic configurations that cannot be reproduced by one repeat unit of the perfect crystal along the tilt axis. Such GB structures cannot be obtained using the γ-surface method, although it is most commonly used for exploring low-energy GB structures. These results highlight the importance of using simulation cells with multiple repeat units along the tilt axis and of performing the SA method with high-accuracy interatomic potentials transferable to GBs.