论文标题
材料图形变压器可预测可靠不确定性的无机反应的结果
Materials Graph Transformer predicts the outcomes of inorganic reactions with reliable uncertainties
论文作者
论文摘要
材料发现的常见瓶颈是综合。尽管最近的方法学进步导致了设计新颖材料的能力的重大改善，但研究人员通常仍然依靠试验方法来确定合成程序。在这项工作中，我们开发了一个预测固态反应的主要产物的模型。这种方法的基本特征是构建固定长度，学习的反应表示。前体表示为“反应图”上的节点，并使用节点之间的消息操作来体现反应混合物中前体之间的相互作用。通过消融研究，结果表明，该框架不仅胜过较小的物理动机基线方法，而且更可靠地评估了其预测中的不确定性。
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.
