Local atomic environment (LAE) representations have enabled a deeper understanding of complex alloy configurations, by enabling machine learning and data science approaches to previously inaccessible tasks. For example, recent developments have used site-centered descriptors to interpret the structurally complex LAEs present in polycrystalline grain boundary (GB) networks and learn the spectrum of grain boundary segregation energies available across many binary alloys. One limitation of many LAE representations is their “site-focus”, which limits their ability to capture chemical complexity at the atomic scale. This paper introduces a modified “bond-focused” LAE representation, which is used to assess pair-wise solute interactions in the topologically complex environments of grain boundaries. This approach opens the pathway to learning models, which we develop here to construct a large-scale database of spectral parameters describing grain boundaries in binary alloys.

中文翻译：

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用于高通量溶质相互作用谱分析的“以键为中心”的局部原子环境表示


局部原子环境 (LAE) 表示通过使用机器学习和数据科学方法来完成以前无法完成的任务，使人们能够更深入地了解复杂的合金配置。例如，最近的进展使用以位点为中心的描述符来解释多晶晶界 (GB) 网络中存在的结构复杂的 LAE，并了解许多二元合金中可用的晶界偏析能的频谱。许多 LAE 表示的一个局限性是它们的“位点聚焦”，这限制了它们在原子尺度捕获化学复杂性的能力。本文介绍了一种改进的“以键为中心”的 LAE 表示法，该表示法用于评估晶界拓扑复杂环境中的成对溶质相互作用。这种方法开辟了学习模型的途径，我们在这里开发该模型是为了构建描述二元合金晶界的光谱参数的大规模数据库。