  The ability to efficiently predict adsorption properties of zeolites can be
of large benefit in accelerating the design process of novel materials. The
existing configuration space for these materials is wide, while existing
molecular simulation methods are computationally expensive. In this work, we
propose a model which is 4 to 5 orders of magnitude faster at adsorption
properties compared to molecular simulations. To validate the model, we
generated datasets containing various aluminium configurations for the MOR,
MFI, RHO and ITW zeolites along with their heat of adsorptions and Henry
coefficients for CO$_2$, obtained from Monte Carlo simulations. The predictions
obtained from the Machine Learning model are in agreement with the values
obtained from the Monte Carlo simulations, confirming that the model can be
used for property prediction. Furthermore, we show that the model can be used
for identifying adsorption sites. Finally, we evaluate the capability of our
model for generating novel zeolite configurations by using it in combination
with a genetic algorithm.
