Symmetry-informed graph neural networks for carbon dioxide isotherm and adsorption prediction in aluminum-substituted zeolites

Abstract

Accurately predicting adsorption properties in nanoporous materials using deep learning models remains a challenging task. This challenge becomes even more pronounced when attempting to generalize to structures that were not part of the training data. In this work, we introduce SymGNN, a graph neural network architecture that leverages material symmetries to improve adsorption property prediction. By incorporating symmetry operations into the message-passing mechanism, our model enhances parameter sharing across different zeolite topologies, leading to improved generalization. We evaluate SymGNN on both interpolation and generalization tasks, using samples with varying Si/Al distributions from 108 zeolite topologies for interpolation and assessing generalization on two unseen frameworks. SymGNN successfully captures key adsorption trends, including the influence of both the framework and aluminium distribution on CO₂ adsorption. Furthermore, we apply our model to the characterization of experimental adsorption isotherms, using a genetic algorithm to infer likely aluminium distributions. Our results highlight the effectiveness of machine learning models trained on simulations for studying real materials and suggest promising directions for fine-tuning with experimental data and generative approaches for the inverse design of multifunctional nanomaterials.