基于一般完备等距不变量的图用于材料性质预测
Material Property Prediction using Graphs based on Generically Complete Isometry Invariants
AI总结：
本文提出基于PDD的Distribution Graph，以不超过不对称单元的顶点表示晶体，在CGCNN和ALIGNN上降低材料性质预测误差并显著减少图规模。
摘要：
结构-性质假说认为，所有材料的性质都由其底层晶体结构决定。主要障碍在于传统晶体表示的歧义性，这些表示基于不完整或不连续的描述符，会导致假阴性或假阳性。这种歧义已由超快速的逐点距离分布（Pointwise Distance Distribution，PDD）解决，PDD在全球最大的真实材料集合（Cambridge Structural Database，剑桥结构数据库）中区分了所有周期结构。此前，性质预测的最先进结果由基于周期晶体各种图表示的图神经网络取得，其中包括顶点位于晶体单胞中所有原子上的Crystal Graph。本工作将Pointwise Distance Distribution适配到一种更简单的图，其顶点集不大于晶体结构的不对称单元。在Materials Project和Jarvis-DFT数据集上使用CGCNN和ALIGNN时，与Crystal Graph相比，新的Distribution Graph将平均绝对误差降低了0.6%-12%，而顶点数仅为后者的44%-88%。该图的超参数选择方法得到Pointwise Distance Distribution理论结果的支持，并随后通过实验得到验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
