Prediction of interface structures and energies via virtual screening.
Prediction of interface structures and energies via virtual screening.
复制标题
通过虚拟筛选对界面结构和能量的预测。
DOI：
10.1126/sciadv.1600746
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发表时间：
2016-11
期刊：
影响因子：
13.6
通讯作者：
Mizoguchi T
中科院分区：
文献类型：
--
作者：
Kiyohara S;Oda H;Miyata T;Mizoguchi T
一种虚拟筛选方法，在确定界面结构的同时，速度最大提高了数万倍。界面显著影响材料的性质，因为它们的原子组态不同。因此，确定界面的原子结构是材料研究中最重要的任务之一。然而，确定界面结构通常需要大量的计算。如果能够有效地预测界面结构，将极大地促进我们对产生界面性质的机理的理解，这将为材料界面的设计铺平道路。使用一种基于机器学习的虚拟筛选方法，我们展示了一种确定界面能量和结构的强大技术。根据4个界面的训练数据进行非线性回归得到的结果，预测了另外13个界面的结构和能量。我们的方法的效率比以前报道的方法高出几百到几万倍。由于本方法使用几何因素，如键长和原子密度，作为回归分析的描述符，本文提出的方法是稳健和通用的，并有望有助于理解任何界面的性质。
A virtual screening method achieved a maximum boost in speed of several tens of thousands–fold while determining the interface structure. Interfaces markedly affect the properties of materials because of differences in their atomic configurations. Determining the atomic structure of the interface is therefore one of the most significant tasks in materials research. However, determining the interface structure usually requires extensive computation. If the interface structure could be efficiently predicted, our understanding of the mechanisms that give rise to the interface properties would be significantly facilitated, and this would pave the way for the design of material interfaces. Using a virtual screening method based on machine learning, we demonstrate a powerful technique to determine interface energies and structures. On the basis of the results obtained by a nonlinear regression using training data from 4 interfaces, structures and energies for 13 other interfaces were predicted. Our method achieved an efficiency that is more than several hundred to several tens of thousand times higher than that of the previously reported methods. Because the present method uses geometrical factors, such as bond length and atomic density, as descriptors for the regression analysis, the method presented here is robust and general and is expected to be beneficial to understanding the nature of any interface.