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

TL;DR

This paper introduces a machine learning model that predicts CO2 adsorption in zeolites with high speed and accuracy, significantly outperforming traditional molecular simulations and aiding in material design.

Contribution

The study presents a fast, accurate ML model for adsorption prediction, validated on multiple zeolite types, and demonstrates its use in site identification and generative design with genetic algorithms.

Findings

Model is 4-5 orders of magnitude faster than molecular simulations.

Predictions closely match Monte Carlo simulation results.

Model can identify adsorption sites and assist in zeolite design.

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.