<ul><li>Accurately predicting adsorption properties in nanoporous materials using Deep Learning models remains a challenging task.</li><li>SymGNN is a graph neural network architecture that leverages material symmetries to improve adsorption property prediction.</li><li>The model successfully captures key adsorption trends, including the influence of both the framework and aluminium distribution on CO2 adsorption.</li><li>The study suggests promising directions for fine-tuning with experimental data and generative approaches for the inverse design of multifunctional nanomaterials.</li></ul>

Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites

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