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Abstract：
Metallic alloys often form phases-known as solid solutions-in which chemical elements are spread out on the same crystal lattice in an almost random manner. The tendency of certain chemical motifs to be more common than others is known as chemical short-range order (SRO), and it has received substantial consideration in alloys with multiple chemical elements present in large concentrations due to their extreme configurational complexity (e.g., high-entropy alloys). SRO renders solid solutions \"slightly less random than completely random,\" which is a physically intuitive picture, but not easily quantifiable due to the sheer number of possible chemical motifs and their subtle spatial distribution on the lattice. Here, we present a multiscale method to predict and quantify the SRO state of an alloy with atomic resolution, incorporating machine learning techniques to bridge the gap between electronic-structure calculations and the characteristic length scale of SRO. The result is an approach capable of predicting SRO length scale in agreement with experimental measurements while comprehensively correlating SRO with fundamental quantities such as local lattice distortions. This work advances the quantitative understanding of solid-solution phases, paving the way for the rigorous incorporation of SRO length scales into predictive mechanical and thermodynamic models.
摘要：
金属合金通常形成相-称为固溶体-其中化学元素以几乎随机的方式散布在相同的晶格上。某些化学基序比其他化学基序更常见的趋势被称为化学短程有序(SRO)。并且由于其极端的构型复杂性，在具有高浓度存在的多种化学元素的合金中，它已经得到了充分的考虑(例如，高熵合金）。SRO使固溶体的随机性略低于完全随机，“这是一幅物理直观的画面，但由于可能的化学基序的数量及其在晶格上的细微空间分布，因此不易量化。这里,我们提出了一种多尺度方法来预测和量化具有原子分辨率的合金的SRO状态，结合机器学习技术，弥合电子结构计算和SRO特征长度尺度之间的差距。结果是一种能够与实验测量结果一致地预测SRO长度尺度的方法，同时将SRO与诸如局部晶格畸变之类的基本量全面关联。这项工作推进了对固溶相的定量理解，为将SRO长度尺度严格纳入预测机械和热力学模型铺平了道路。
