Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites
This work accelerates the design of novel materials for carbon capture by providing a fast predictive tool for zeolite adsorption properties, though it is incremental as it applies an existing machine learning method to a specific domain problem.
The researchers tackled the problem of predicting carbon dioxide adsorption properties in aluminium-exchanged zeolites, which is computationally expensive with existing methods, and developed a Graph Neural Network model that is 4 to 5 orders of magnitude faster than molecular simulations while maintaining agreement with Monte Carlo simulation results.
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.