论文标题
材料属性预测使用基于一般完整等轴测不变的图形预测
Material Property Prediction using Graphs based on Generically Complete Isometry Invariants
论文作者
论文摘要
结构质体假设说，所有材料的性质均由潜在的晶体结构确定。主要障碍是基于不完整或不连续的描述符的传统晶体表示的歧义，这些描述允许虚假负面或假阳性。这种歧义是通过超快速距离分布（PDD）解决的，该分布区分了世界上最大的真实材料集合（剑桥结构数据库）中的所有周期性结构。先前，基于周期性晶体的各种图表，包括晶体单位单元中所有原子的晶体图，包括晶体图，包括晶体图，包括图形的各种图表，先前通过图神经网络实现了属性预测的最新结果。这项工作适应了一个更简单的图形的距离分布，其顶点集不比晶体结构的不对称单元大。新的分布图将均值吸收性误解减少为0.6 \％-12 \％，而与晶体图相比，使用CGCNN和Alignn在材料项目和jarvis-dft数据集上时，将44 \％-88 \％的顶点数量减少。图表的超参数选择方法得到了点式距离分布的理论结果的支持，然后在实验上是合理的。
The structure-property hypothesis says that the properties of all materials are determined by an underlying crystal structure. The main obstacle was the ambiguity of conventional crystal representations based on incomplete or discontinuous descriptors that allow false negatives or false positives. This ambiguity was resolved by the ultra-fast Pointwise Distance Distribution (PDD), which distinguished all periodic structures in the world's largest collection of real materials (Cambridge Structural Database). The state-of-the-art results in property predictions were previously achieved by graph neural networks based on various graph representations of periodic crystals, including the Crystal Graph with vertices at all atoms in a crystal unit cell. This work adapts the Pointwise Distance Distribution for a simpler graph whose vertex set is not larger than the asymmetric unit of a crystal structure. The new Distribution Graph reduces mean-absolute-error by 0.6\%-12\% while having 44\%-88\% of the number of vertices when compared to the crystal graph when applied on the Materials Project and Jarvis-DFT datasets using CGCNN and ALIGNN. Methods for hyper-parameters selection for the graph are backed by the theoretical results of the Pointwise Distance Distribution and are then experimentally justified.
