  Fundamental differences between materials originate from the unique nature of
their constituent chemical elements. Before specific differences emerge
according to the precise ratios of elements in a given crystal structure, a
material can be represented by the set of its constituent chemical elements. By
working at the level of the periodic table, assessment of materials at the
level of their phase fields reduces the combinatorial complexity to accelerate
screening, and circumvents the challenges associated with composition-level
approaches such as poor extrapolation within phase fields, and the
impossibility of exhaustive sampling. This early stage discrimination combined
with evaluation of novelty of phase fields aligns with the outstanding
experimental challenge of identifying new areas of chemistry to investigate, by
prioritising which elements to combine in a reaction. Here, we demonstrate that
phase fields can be assessed with respect to the maximum expected value of a
target functional property and ranked according to chemical novelty. We develop
and present PhaseSelect, an end-to-end machine learning model that combines the
representation, classification, regression and ranking of phase fields. First,
PhaseSelect constructs elemental characteristics from the co-occurrence of
chemical elements in computationally and experimentally reported materials,
then it employs attention mechanisms to learn representation for phase fields
and assess their functional performance. At the level of the periodic table,
PhaseSelect quantifies the probability of observing a functional property,
estimates its value within a phase field and also ranks a phase field novelty,
which we demonstrate with significant accuracy for three avenues of materials
applications for high-temperature superconductivity, high-temperature
magnetism, and targeted bandgap energy.
