We provide an overview of how to apply statistical learning methods to directly track the role of alloying additions in the multiscale properties of alloys. This leads to a mapping process analogous to the Periodic Table where the resulting visualization scheme exhibits the grouping and proximity of elements based on their impact on the properties of alloys. Unlike the conventional Periodic Table of elements, the distance between neighboring elements in our Alloy Periodic Table uncovers relationships in a complex high-dimensional information space that would not be easily seen otherwise. We embed this machine learning approach with an epistemic uncertainty assessment between data. We provide examples of how this data-driven exploratory platform appears to capture the alloy chemistry of known engineering alloys as well as to provide potential new directions for tuning chemistry for enhanced performance, consistent with accepted mechanistic paradigms governing alloy mechanical properties.

中文翻译：

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为合金设计设计元素周期表：利用机器学习在多尺度信息空间中导航

我们概述了如何应用统计学习方法来直接跟踪合金添加物在合金多尺度特性中的作用。这导致了类似于元素周期表的映射过程，其中生成的可视化方案根据元素对合金性能的影响来展示元素的分组和接近度。与传统的元素周期表不同，我们合金周期表中相邻元素之间的距离揭示了复杂的高维信息空间中的关系，否则很难看到。我们将这种机器学习方法嵌入到数据之间的认知不确定性评估中。