  Machine learning has the potential to accelerate materials discovery by
accurately predicting materials properties at a low computational cost.
However, the model inputs remain a key stumbling block. Current methods
typically use descriptors constructed from knowledge of either the full crystal
structure -- therefore only applicable to materials with already characterised
structures -- or structure-agnostic fixed-length representations
hand-engineered from the stoichiometry. We develop a machine learning approach
that takes only the stoichiometry as input and automatically learns appropriate
and systematically improvable descriptors from data. Our key insight is to
treat the stoichiometric formula as a dense weighted graph between elements.
Compared to the state of the art for structure-agnostic methods, our approach
achieves lower errors with less data.
