Abstract The generation of the vacancy involving the bond breaking/re-formation occurs naturally in the material. Here, we present a framework for automatically computing the vacancy-formation energy ( E f ) and for analyzing the bonding environment concealed in the E f by using an artificial neural network (ANN). The ‘effective’ bonding that determines the energy of the system and the E f will be clarified. The phase-change memory material GeTe is used as a case study. Firstly, 791 Ge-vacancy containing GeTe structures are studied and a large data set of the formation energy of the Ge-vacancy is obtained, which is helpful to understand the vacancy-induced issue of the amorphous GeTe including the resistance drift, etc. By using the ANN fitting based on the large energy data set, a bonding picture that is applicable to both the crystalline and the amorphous state of GeTe is predicted. In terms of the contribution to the formation energy of the vacancy, the weight ratio of the bond with length of 3.0–3.6 A and 3.6–4.5 A can be approximated as 6:1. The bonding information is further confirmed by using the first-principles electronic structure analysis on the randomly chosen samples. The bonding analysis using the ANN method based on a large vacancy-formation-energy data set is demonstrated to be a novel alternative technique to understand the bonding in the material. The proposed framework can be applied to a wide range of materials.

中文翻译：

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空位形成能及其与固体键合环境的联系：高通量计算和机器学习研究

摘要 涉及键断裂/重新形成的空位的产生在材料中自然发生。在这里，我们提出了一个框架，用于自动计算空位形成能量 (E f ) 并使用人工神经网络 (ANN) 分析隐藏在 E f 中的键合环境。将阐明决定系统能量和 E f 的“有效”键合。相变存储材料 GeTe 用作案例研究。首先，研究了含GeTe结构的791 Ge空位，获得了Ge空位形成能的大数据集，有助于理解非晶GeTe空位引起的问题，包括电阻漂移等。使用基于大能量数据集的 ANN 拟合，预测了适用于 GeTe 的晶态和非晶态的键合图片。在对空位形成能的贡献方面，长度为3.0-3.6 A和3.6-4.5 A的键的重量比可以近似为6:1。通过对随机选择的样品使用第一性原理电子结构分析进一步确认键合信息。使用基于大量空位形成能量数据集的 ANN 方法的键合分析被证明是一种了解材料键合的新型替代技术。所提出的框架可以应用于广泛的材料。5 A 可以近似为 6:1。通过对随机选择的样品使用第一性原理电子结构分析进一步确认键合信息。使用基于大量空位形成能量数据集的 ANN 方法的键合分析被证明是一种了解材料键合的新型替代技术。所提出的框架可以应用于广泛的材料。5 A 可以近似为 6:1。通过对随机选择的样品使用第一性原理电子结构分析进一步确认键合信息。使用基于大量空位形成能量数据集的 ANN 方法的键合分析被证明是一种了解材料键合的新型替代技术。所提出的框架可以应用于广泛的材料。