  Recent advances in machine learning techniques have made it possible to use
high-throughput screening to identify novel materials with specific properties.
However, the large number of potential candidates produced by these techniques
can make it difficult to select the most promising ones. In this study, we
develop the oxidation state probability (OSP) method which evaluates ternary
compounds based on the probability (the OSP metric) of each element to adopt
the required oxidation states for fulfilling charge neutrality. We compare this
model with Roost and the Fourier-transformed crystal properties (FTCP)-based
synthesizability score. Among the top 1000 systems with the most database
entries in Materials Project (MP), more than 500 systems exhibit an attested
compound among the top 3 compositions when ranked by the OSP metric. We find
that the OSP method shows promising results for certain classes of ternary
systems, especially those containing nonmetals, s-block, or transition metals.
When applied to the Cu-In-Te ternary system, an interesting system for
thermoelectric applications, the OSP method predicted the synthesizability of
CuIn$_3$Te$_5$ without prior knowledge, and we have successfully synthesized
CuIn$_3$Te$_5$ in experiment. Our method has the potential to accelerate the
discovery of novel compounds by providing a guide for experimentalists to
easily select the most synthesizable candidates from an arbitrarily large set
of possible chemical compositions.
