Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(II) and Cd(II) ions
Published in Journal of Materials Chemistry A, 2023
Recommended citation: Jaffari, Z. H., Abbas, A., Umar, M., Kim, E-S., & Cho, K. H. (2023). Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(II) and Cd(II) ions https://doi.org/10.1039/D3TA00019B
In this paper, we employed Crystal Graph Neural Network to predict the adsorption capability of two- dimensional niobium carbide (Nb2 CTx) at arbitrary adsorption sites for lead (Pb(II )) and cadmium (Cd( II)) ions. The results indicated that Pb(II ) ions had a higher adsorption energy than Cd(II) ions with a mean absolute error and root-mean-squared error less than 0.09 eV and 0.16 eV, respectively. The proposed CGCNN model has a similar prediction to the ab initio DFT calculations, yet significantly fast and economical. Finally, the adsorption capability of Nb2CTx synthesized using a fluorine-free route was also experimentally verified, and the results were consistent with DFT calculations and CGCNN predictions. In addition, the synthesized Nb2CTx exhibited a higher recycling potential over five successive runs. Collectively, these findings indicated that the proposed technique is highly efficient in investigating the adsorption performance of materials and can be further extended for use in the removal of other hazardous pollutants from aqueous environments.