Phase Classification of Multi-Principal Element Alloys via Interpretable Machine Learning

This dataset contains 1821 compositions ranging from binary to multi-component alloys along with phase information by referring to several previous reports that compiled experimental data from the published literature.A total of 125 variables for each observation were generated by the Magpie program and down-selected to 12 variables based on linear Pearson correlation coefficient and non-linear normalized mutual information analyses. The down-selected variables can be subdivided into three categories: (1) those that are chemistry-agnostic (e.g., MixingEntropy),(2) those that depend on element pairs (e.g., DeltaHf), (3) those that are depend on chemistry (e.g., maxdiff_Electronegativity).