The structure and energy of grain boundaries (GBs) are essential for predicting the properties of polycrystalline materials. In this work, we use high-throughput density functional theory calculations workflow to construct the Grain Boundary Database (GBDB), the largest database of DFT-computed grain boundary properties to date. The database currently encompasses 327 GBs of 58 elemental metals, including 10 common twist or symmetric tilt GBs for body-centered cubic (bcc) and face-centered cubic (fcc) systems and the $\Sigma$7 [0001] twist GB for hexagonal close-packed (hcp) systems. In particular, we demonstrate a novel scaled-structural template approach for HT GB calculations, which reduces the computational cost of converging GB structures by a factor of $\sim 3-6$. The grain boundary energies and work of separation are rigorously validated against previous experimental and computational data. Using this large GB dataset, we develop an improved predictive model for the GB energy of different elements based on the cohesive energy and shear modulus. The open GBDB represent a significant step forward in the availability of first principles GB properties, which we believe would help guide the future design of polycrystalline materials.

中文翻译：

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元素金属的晶界特性

晶界 (GB) 的结构和能量对于预测多晶材料的性能至关重要。在这项工作中，我们使用高通量密度泛函理论计算工作流来构建晶界数据库 (GBDB)，这是迄今为止最大的 DFT 计算晶界特性数据库。该数据库目前包含 327 GB 的 58 种元素金属，包括用于体心立方 (bcc) 和面心立方 (fcc) 系统的 10 个常见扭曲或对称倾斜 GB 以及用于六边形闭合的 $\Sigma$7 [0001] 扭曲 GB打包 (hcp) 系统。特别是，我们展示了一种用于 HT GB 计算的新型缩放结构模板方法，该方法将收敛 GB 结构的计算成本降低了 $\sim 3-6 $。晶界能量和分离功根据先前的实验和计算数据进行了严格验证。使用这个大型 GB 数据集，我们基于内聚能和剪切模量开发了不同元素的 GB 能量的改进预测模型。开放的 GBDB 代表了在第一性原理 GB 特性的可用性方面向前迈出的重要一步，我们相信这将有助于指导多晶材料的未来设计。