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Efficiently predicting properties of porous crystalline materials has greatpotential to accelerate the high throughput screening process for developingnew materials, as simulations carried out using first principles model areoften computationally expensive. To effectively make use of Deep Learningmethods to model these materials, we need to utilize the symmetries present inthe crystals, which are defined by their space group. Existing methods forcrystal property prediction either have symmetry constraints that are toorestrictive or only incorporate symmetries between unit cells. In addition,these models do not explicitly model the porous structure of the crystal. Inthis paper, we develop a model which incorporates the symmetries of the unitcell of a crystal in its architecture and explicitly models the porousstructure. We evaluate our model by predicting the heat of adsorption of CO$_2$for different configurations of the mordenite zeolite. Our results confirm thatour method performs better than existing methods for crystal propertyprediction and that the inclusion of pores results in a more efficient model.

Equivariant Parameter Sharing for Porous Crystalline Materials