Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry. Here, we recapitulate the graph construction for crystalline (periodic) materials and investigate its impact on the GNNs model performance. We suggest the asymmetric unit cell as a representation to reduce the number of atoms by using all symmetries of the system. This substantially reduced the computational cost and thus time needed to train large graph neural networks without any loss in accuracy. Furthermore, with a simple but systematically built GNN architecture based on message passing and line graph templates, we introduce a general architecture (Nested Graph Network, NGN) that is applicable to a wide range of tasks. We show that our suggested models systematically improve state-of-the-art results across all tasks within the MatBench benchmark. Further analysis shows that optimized connectivity and deeper message functions are responsible for the improvement. Asymmetric unit cells and connectivity optimization can be generally applied to (crystal) graph networks, while our suggested nested graph framework will open new ways of systematic comparison of GNN architectures.

翻译：图神经网络（GNN）已被广泛应用于材料科学和化学领域的各类任务中。本文重新阐述了晶体（周期）材料的图构建方法，并探究了其对GNN模型性能的影响。我们提出采用不对称晶胞作为表示方法，通过利用系统的全部对称性来减少原子数量。这显著降低了计算成本以及训练大规模图神经网络所需的时间，且精度未受任何损失。此外，基于消息传递与线图模板这一简单但系统构建的GNN架构，我们引入了一种适用于广泛任务的通用架构（嵌套图网络，NGN）。研究表明，我们所提出的模型在MatBench基准测试的所有任务上系统性地提升了现有最优结果。进一步分析表明，连通性优化与更深层消息函数是性能提升的关键因素。不对称晶胞与连通性优化可普遍应用于（晶体）图网络，而我们所提出的嵌套图框架将为GNN架构的系统性比较开辟新途径。