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A common bottleneck for materials discovery is synthesis. While recentmethodological advances have resulted in major improvements in the ability topredicatively design novel materials, researchers often still rely ontrial-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 toembody the interactions between precursors in the reaction mixture. Through anablation study, it is shown that this framework not only outperforms lessphysically-motivated baseline methods but also more reliably assesses theuncertainty in its predictions.

Materials Graph Transformer predicts the outcomes of inorganic reactions with reliable uncertainties