论文标题
代表域独立材料发现的公式图自发项网络发现
Formula graph self-attention network for representation-domain independent materials discovery
论文作者
论文摘要
机器学习（ML）在材料属性预测中的成功在很大程度上取决于材料如何用于学习。存在两个主要的材料描述符族，一个代表中的晶体结构，另一种仅使用化学计量信息，希望发现新材料。尤其是图形神经网络（GNN）在预测化学精度内的材料特性方面表现出色。但是，由于各个物质表示之间的重叠很小，当前的GNN仅限于上述两种途径之一。在这里，我们介绍了一个新的公式图概念，该概念统一了仅化学计量和基于结构的材料描述符。我们进一步开发了一个自我发项的综合GNN，该GNN吸收了公式图，并表明所提出的架构会在两个域之间传递可转移的材料嵌入。我们的模型可以胜过一些先前提出的结构不可吻合的模型及其基于结构的模型，同时表现出更好的样品效率和更快的收敛性。最后，将模型应用于具有挑战性的范例中，以预测材料的复杂介电函数，并提名可能表现出可能表现出Epsilon-Near-Zero现象的新物质。
The success of machine learning (ML) in materials property prediction depends heavily on how the materials are represented for learning. Two dominant families of material descriptors exist, one that encodes crystal structure in the representation and the other that only uses stoichiometric information with the hope of discovering new materials. Graph neural networks (GNNs) in particular have excelled in predicting material properties within chemical accuracy. However, current GNNs are limited to only one of the above two avenues owing to the little overlap between respective material representations. Here, we introduce a new concept of formula graph which unifies stoichiometry-only and structure-based material descriptors. We further develop a self-attention integrated GNN that assimilates a formula graph and show that the proposed architecture produces material embeddings transferable between the two domains. Our model can outperform some previously proposed structure-agnostic models and their structure-based counterparts while exhibiting better sample efficiency and faster convergence. Finally, the model is applied in a challenging exemplar to predict the complex dielectric function of materials and nominate new substances that potentially exhibit epsilon-near-zero phenomena.
