Machine learning and DFT investigation of CO, CO2 and CH4 adsorption on pristine and defective two-dimensional magnesene

Abstract

Adsorption study of environmentally toxic small gas molecules on two-dimensional (2D) materials plays a significant role in analyzing the performance of sensors. In this work, density functional theory (DFT) and machine learning (ML) techniques have been employed to systematically study the adsorption properties of CO, CO₂, and CH₄ gas molecules on the pristine and defective planar magnesium monolayer, known as magnesene (2D-Mg). The DFT analysis showed that mechanically robust 2D-Mg retains its metallicity in the presence of both mono and di-vacancy defects. Our observations have shown that 2D-Mg, whether defective or pristine, exhibits distinct adsorption behaviors towards CO, CO₂, and CH₄ gas molecules, including varying chemisorption and physisorption, charge transfer, and distance from the gas molecules. When analyzing the recovery time of gas molecules at room temperature, it is clear that adsorption energy has a direct correlation with the adsorption–desorption cycles, and CH₄ possesses an ultra-low recovery time (15.27 ps) compared to CO₂ (1.04 ns) and CO (0.90 μs) molecules. The analysis showed that defects do not have a significant impact on the work function of 2D-Mg. However, the work function decreased upon adsorption of CH₄, resulting in improved sensitivity due to changes in the electronic properties. Additionally, we explored supervised ML regression models to evaluate their ability to act as a surrogate for the DFT-based adsorption energy calculation. Using both system statistics and smooth overlap of atomic position (SOAP)-based featurization, we observed that adsorption energies can be predicted with a mean absolute error of 0.10 eV.