DFT and Monte Carlo Simulation Study of Drug Molecules as Corrosion Inhibitors on Iron Surfaces

In this study, density functional theory (DFT) was used to evaluate the corrosion inhibition potential of six commonly used molecular drugs: diazepam, chlorpromazine, amitriptyline, cimetidine, metformin, and ranitidine. These compounds were chosen based on their structural features and electron-rich functional groups, which are believed to promote interactions with metal surfaces. Quantum chemical calculations were performed in both gas and aqueous phases using the 6-311[Formula: see text]G (d, p) basis set. Key electronic descriptors, including HOMO, LUMO, energy gap (Eg), dipole moment, ionization energy, electron affinity, hardness, softness, and chemical potential, were calculated to assess molecular reactivity and predict inhibition efficiency. Additional analysis involved molecular electrostatic potential (MEP) mapping, natural bond orbital (NBO) analysis, and Fukui function calculations to identify electron-rich (nucleophilic) and electron-deficient (electrophilic) regions of each molecule. Metropolis Monte Carlo simulations with Materials Studio were conducted to estimate the adsorption behavior and configuration energies of the drug molecules on iron surfaces. The results show that chlorpromazine and amitriptyline demonstrate the strongest inhibition potential due to favorable electron donation and high molecular softness, while diazepam exhibits the least effectiveness due to its higher energy gap and lower adsorption tendencies. This research highlights the feasibility of using DFT and molecular simulations to predict corrosion inhibition behavior, which is especially valuable for screening drug molecules with dual functions — both therapeutic and protective — particularly in resource-limited settings. The computational approach presented offers a reliable framework for the theoretical evaluation of corrosion inhibitors prior to experimental validation.