论文标题
晶体结构的模棱两可的网络
Equivariant Networks for Crystal Structures
论文作者
论文摘要
具有深层模型的监督学习在材料科学中的应用中具有巨大的潜力。最近，在这种情况下使用了图神经网络，从分子模型中汲取了直接的灵感。但是，材料通常比分子更具结构化，这是这些模型不利用的特征。在这项工作中，我们介绍了一类模型，这些模型相对于晶体对称组而言。我们通过定义可以与更通用的置换组一起使用的消息传递操作的概括来做到这一点，或者可以将其视为在晶体图上定义表达性卷积操作。从经验上讲，这些模型通过最先进的财产预测任务实现了竞争成果。
Supervised learning with deep models has tremendous potential for applications in materials science. Recently, graph neural networks have been used in this context, drawing direct inspiration from models for molecules. However, materials are typically much more structured than molecules, which is a feature that these models do not leverage. In this work, we introduce a class of models that are equivariant with respect to crystalline symmetry groups. We do this by defining a generalization of the message passing operations that can be used with more general permutation groups, or that can alternatively be seen as defining an expressive convolution operation on the crystal graph. Empirically, these models achieve competitive results with state-of-the-art on property prediction tasks.
