Automated determination of grain boundary energy and potential-dependence using the OpenKIM framework
Automated determination of grain boundary energy and potential-dependence using the OpenKIM framework
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DOI：
10.1016/j.commatsci.2023.112057
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发表时间：
2022-12
影响因子：
3.3
通讯作者：
Brendon Waters;Daniel S. Karls;I. Nikiforov;R. Elliott;E. Tadmor;B. Runnels
中科院分区：
文献类型：
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作者：
Brendon Waters;Daniel S. Karls;I. Nikiforov;R. Elliott;E. Tadmor;B. Runnels
我们提出了一种系统的方法，建立在原子间模型开放知识库（OpenKIM）框架（https://openkim.org）中，用于量化任意原子间势（ip）， GB特征以及晶格结构和物种的晶界（GB）特性。该框架目前在立方材料中生成对称倾斜gb的结果，但可以很容易地扩展到其他类型的边界。本文介绍了225个IPs自动生成的Al、Ni、Cu、Fe、Mo的GB能量数据；该系统安装在openkim.org上，并将继续为上传到OpenKIM的所有新ip生成结果。原子计算结果与晶格匹配模型进行了比较，后者是一种近似GB能量的半解析几何模型。可以确定的是，所有ip预测的能量（对于给定的边界类型是稳定的）与模型的能量密切相关，直到一个乘法因子。由此可以得出结论：相对于IP的选择，GB能量与倾斜角的定性形式更多地受几何形状的支配，但IP对能量水平有强烈的影响。OpenKIM中GB能量预测在ip集合中的分布为经典ip的GB能量预测提供了一个不确定性度量。
We present a systematic methodology, built within the Open Knowledgebase of Interatomic Models (OpenKIM) framework (https://openkim.org), for quantifying properties of grain boundaries (GBs) for arbitrary interatomic potentials (IPs), GB character, and lattice structure and species. The framework currently generates results for symmetric tilt GBs in cubic materials, but can be readily extended to other types of boundaries. In this paper, GB energy data are presented that were generated automatically for Al, Ni, Cu, Fe, and Mo with 225 IPs; the system is installed on openkim.org and will continue to generate results for all new IPs uploaded to OpenKIM. The results from the atomistic calculations are compared to the lattice matching model, which is a semi-analytic geometric model for approximating GB energy. It is determined that the energy predicted by all IPs (that are stable for the given boundary type) correlate closely with the energy from the model, up to a multiplicative factor. It thus is concluded that the qualitative form of the GB energy versus tilt angle is dominated more by geometry than the choice of IP, but that the IP can strongly affect the energy level. The spread in GB energy predictions across the ensemble of IPs in OpenKIM provides a measure of uncertainty for GB energy predictions by classical IPs.