Designing a Periodic Table for Alloy Design: Harnessing Machine Learning to Navigate a Multiscale Information Space

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.