Au-Ni合金模拟菊池花样的成分与有序度依赖演化
Composition- and Ordering-Dependent Evolution of Simulated Kikuchi Patterns of Au-Ni Alloys
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中文总结 AI 辅助
该研究分析Au-Ni合金模拟菊池花样的成分、有序度等影响因素，明确归一化强度分布和带宽可区分菊池花样，有望用于未来基于表示学习的索引方法。
中文摘要 AI 辅助
理解菊池花样如何随材料的细微变化而变化，对于开发基于机器学习（ML）的索引方法至关重要，这些方法适用于结构和化学复杂的情况，例如具有潜在亚晶格有序性的相，或具有偏聚（缺陷相）的（亚）稳定缺陷。本文系统分析了二元Au-Ni体系的模拟菊池花样，以研究晶格参数、化学成分、部分位点占据和有序度的影响。研究考虑了从纯镍到纯金的全部成分范围，包括假设的有序L1₂ Au₃Ni结构。通过与实验花样对比优化了模拟参数。归一化互相关显示其对花样间细微差异的敏感性有限。EMsoft模拟结果表明，随着金含量增加，平均强度系统地升高，且更多衍射斑显著贡献强度。从99%金含量样品到纯金样品，平均强度出现惊人的四倍增长，凸显了EMsoft在部分位点占据模拟方面的局限性。差异图显示，与更高金含量的样品相比，金含量为20%的样品在{111}和{200}带的归一化强度增强。通过分离晶格参数和化学成分的影响，化学成分对平均强度和强衍射斑数量起主要作用，尽管归一化强度分布受两者共同影响。引入L1₂有序性增加了强衍射斑数量和平均强度，并重新分布了{111}、{200}带及选定晶带轴的归一化强度。归一化强度分布和带宽是区分菊池花样的关键描述符，可被纳入未来基于表示学习的索引方法中。
英文摘要
Understanding how Kikuchi patterns behave with subtle material variations is essential for developing machine learning (ML) based indexing methods for structurally and chemically complex cases, such as phases with potential sub-lattice order or (meta)stable defects with segregation (defect phases). Simulated Kikuchi patterns of the binary Au-Ni system were systematically analysed to investigate the effects of lattice parameter, chemical composition, partial site occupancy and ordering. The full compositional range from pure nickel to pure gold was considered, including a hypothetical ordered L$1_2$ Au$_3$Ni structure. Simulation parameters were optimised by comparison with experimental patterns. Normalized cross correlation showed limited sensibility to subtle differences between patterns. EMsoft simulation results revealed a systematic increase in mean intensity and more reflections contribute significantly as gold content increases. A surprising four-fold increase in mean intensity from 99 % gold to the pure gold sample highlighted limitations of partial site occupancy simulation by EMsoft. Difference maps showed enhanced normalised intensity along the {111} and {200} bands for the sample with 20 % gold compared to higher gold compositions. By isolating lattice parameter and chemical composition effects, chemical composition contributes predominantly to the mean intensity and strong reflections count, although the normalised intensity distribution was affected by both. Introducing L$1_2$ ordering increased the number of strong reflections and mean intensity, and redistributed normalised intensity along {111} and {200} bands and selected zone axis. Normalised intensity distribution and band width, are key descriptions to distinguish the Kikuchi patterns, which can be incorporated in future representation learning based indexing methods.
