Porous crystalline materials have the potential to play a key role in developing solutions for molecular storage, gas separation and carbon adsorption. For these solutions, we need to develop new materials with specific properties. Estimating the properties of such porous materials involves first principle simulation using classical molecular simulations. The computational complexity of these methods can be a barrier to high throughput screening of the potential materials as the space of possible materials is vast. Data-driven methods, specifically machine learning methods based on deep neural networks offer a significant opportunity to significantly scale the simulation of the behavior of these materials. However, to effectively achieve this the Deep Learning models need to utilize the symmetries present in the crystals. Crystals pose specific symmetries that are present in their space group. Existing methods for crystal property prediction either have symmetry constraints that are too restrictive or only incorporate symmetries between unit cells. In addition, these models do not explicitly model the porous structure of the crystal. In this paper, we develop a model which incorporates the symmetries of the unit cell of a crystal in its architecture and explicitly models the porous structure. We evaluate our model by predicting the heat of adsorption of CO$_2$ for different configurations of the Mordenite and ZSM-5 zeolites. Our results confirm that our method performs better than existing methods for crystal property prediction and that the inclusion of pores results in a more efficient model.

翻译：多孔晶体材料在分子存储、气体分离和碳吸附等解决方案的开发中具有关键潜力。为实现这些方案，我们需要开发具有特定属性的新型材料。评估此类多孔材料的性质需借助经典分子模拟进行第一性原理计算。由于潜在材料的空间极为广阔，这些方法的计算复杂性可能成为高通量筛选的障碍。基于深度神经网络的数据驱动方法，特别是机器学习方法，为显著扩展这些材料行为的模拟规模提供了重要机遇。然而，要有效实现这一目标，深度学习模型需要利用晶体中存在的对称性。晶体在其空间群中呈现出特定的对称性。现有的晶体性质预测方法要么对称性约束过于严格，要么仅整合了晶胞间的对称性。此外，这些模型并未显式建模晶体的多孔结构。本文开发了一种模型，该模型在架构中整合了晶体晶胞的对称性，并显式建模了多孔结构。我们通过预测丝光沸石和ZSM-5沸石不同构型下CO₂的吸附热来评估模型。实验结果证实，我们的方法在晶体性质预测方面优于现有方法，且孔隙的引入使得模型更加高效。