材料图Transformer预测无机反应的结果，具有可靠的不确定性
Materials Graph Transformer predicts the outcomes of inorganic reactions with reliable uncertainties
摘要：
材料发现的一个常见瓶颈是合成。虽然最近的方法学进步已经导致预测性设计新材料的能力的重大改进，但研究人员通常仍然依赖于试错法来确定合成程序。在这项工作中，我们开发了一个模型，预测固态反应的主要产品。这种方法的主要特点是构建固定长度的、习得的反应表征。前体被表示为“反应图”上的节点，节点之间的消息传递操作用于体现反应混合物中前体之间的相互作用。通过消融研究，它表明，该框架不仅优于较少的物理动机的基线方法，但也更可靠地评估其预测的不确定性。
英文摘要：
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.
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