  A common bottleneck for materials discovery is synthesis. While recent
methodological advances have resulted in major improvements in the ability to
predicatively design novel materials, researchers often still rely on
trial-and-error approaches for determining synthesis procedures. In this work,
we develop a model that predicts the major product of solid-state reactions.
The cardinal feature of this approach is the construction of fixed-length,
learned representations of reactions. Precursors are represented as nodes on a
`reaction graph', and message-passing operations between nodes are used to
embody the interactions between precursors in the reaction mixture. Through an
ablation study, it is shown that this framework not only outperforms less
physically-motivated baseline methods but also more reliably assesses the
uncertainty in its predictions.
