  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 non-reactive or not just
investigated, though the reactive combinations can be easily collected as
positive datasets from the materials databases. To construct the negative
datasets, we developed a process to select reliable non-reactive combinations
by evaluating the similarity between unreported and reactive combinations. The
machine learning models were trained by both datasets 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 Co2Al1.24Ge1.76.
