  Proposing new materials by atom substitution based on periodic table
similarity is a conventional strategy of searching for materials with desired
property. We introduce a machine learning frame work that promotes this
paradigm to be property-specific and quantitative. It is of peculiar usefulness
in situations where abundance data is accessible for learning general knowledge
but samples for the problem of interest are relatively scarce. We showcase its
usage and viability in the problem of separating high entropy alloys with
different structural phases, for which a very simple data-driven criterion
achieves differentiating ability comparable with widely used empirical
criteria. Its flexibility and generability make it a promising tool in other
material discovery tasks and far beyond.
