ElementalReactivity Maps for Materials Discovery

When searching for novel inorganic materials, limiting the combination of constituent elements can greatly improve the search efficiency. In this study, we used machine learning to predict elemental combinations with high reactivity for materials discovery. The essential issue for such prediction is the uncertainty of whether the unreported combinations are nonreactive or not just investigated, though the reactive combinations can be easily collected as positive data sets from the materials databases. To construct the negative data sets, we developed a process to select reliable nonreactive combinations by evaluating the similarity between unreported and reactive combinations. The machine learning models were trained by both data sets, and the prediction results were visualized by two-dimensional heatmaps: elemental reactivity maps to identify elemental combinations with high reactivity but no reported stable compounds. The maps predicted high reactivity (i.e., synthesizability) for the Co–Al–Ge ternary system, and two novel ternary compounds were synthesized: Co₄Ge_3.19Al_0.81 and Co₂Al_1.26Ge_1.74.