Selective gas adsorption using graphitic carbon nitride: Exploring the role of molecular descriptors by artificial intelligence frameworks.
Journal:
Journal of environmental management
PMID:
39933377
Abstract
Artificial Intelligence (AI) frameworks estimate the adsorption energies of crucial pollutants like CO, O, NO, NO, SOF, HCHO, and CO on Graphitic Carbon Nitride (g-CN) surfaces. The predictive capabilities of two AI-based models, namely, Artificial Neural Network (ANN) and ANN coupled with Grey Wolf Optimization (ANN-GWO), are assessed for this purpose. The frameworks are built over 232 data points of adsorption energy collected from Density Function Theory calculations (DFT). Further, molecular descriptors with two-dimensional and three-dimensional descriptors over molecular surfaces are created, serving as structural input for the AI frameworks. Both models, ANN and ANN-GWO, excel in estimating adsorption energies for polar gases such as CO, achieving prediction errors around 10, while nonpolar gases like NO and HCHO exhibited larger deviations due to electron cloud diffusion. This emphasizes the critical role of molecular polarity in gas-surface interactions. This study underlines the significance of selecting appropriate molecular descriptors for reliable estimation of adsorption characteristics, offering a robust, computationally efficient alternative to conventional methods. The proposed frameworks provide valuable insights into pollutant-gas interactions, paving the way for advancements in material design for environmental applications.