Elemental Reactivity 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: Co4Ge3.19Al0.81 and Co2Al1.26Ge1.74.