Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites

• URL: http://arxiv.org/abs/2403.12659v2
• Date: Thu, 28 Mar 2024 14:19:21 GMT
• Title: Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites
• Authors: Marko Petković, José Manuel Vicent-Luna, Vlado Menkovski, Sofía Calero,
• Abstract summary: We propose a model which is 4 to 5 orders of magnitude faster at adsorbing properties compared to molecular simulations. The predictions obtained from the Machine Learning model are in agreement with the values obtained from the Monte Carlo simulations. We show that the model can be used for identifying adsorbing sites.
• Score: 3.909855210960908
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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