Efficiently predicting properties of porous crystalline materials has great potential to accelerate the high throughput screening process for developing new materials, as simulations carried out using first principles model are often computationally expensive. To effectively make use of Deep Learning methods to model these materials, we need to utilize the symmetries present in the crystals, which are defined by 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 zeolite. 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:为有效预测多孔晶体材料的性质以推动高通量筛选新材料的进程,开展利用深度学习方法建模这些材料时,需要利用晶体中存在的对称性,其对称性由其空间群定义。现有的晶体性质预测方法的对称性约束条件太严格,或只在晶胞之间的对称性方面进行考虑。此外,这些模型并未明确建模晶体的孔隙结构。本文构建了一种模型,在其架构中整合了晶体的晶胞对称性,并明确建模了其孔隙结构。我们通过预测各种莫尔得石沸石构型下CO2吸附热来评估我们的模型。结果表明,与现有的晶体性质预测方法相比,我们的方法表现更佳,并且考虑孔隙导致模型更加高效。