  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.
