Optimizing Extended Tight-Binding Methods for Metal-Surface Interactions

The accurate description of metal-water interfaces is essential for understanding processes in heterogeneous catalysis, electrochemistry, and surface science. Capturing the delicate balance between electrostatic and charge-transfer interactions in these systems, while efficiently sampling configurations to locate minima or approximate thermodynamic ensembles, requires electronic-structure methods that are both accurate and computationally efficient. Density functional tight-binding methods have the potential to strike the right balance, and here we demonstrate how systematic parameter optimization within the GFN1-xTB framework improves the description of water-metal interactions. Using previously published reference data for five metals (Cu, Ag, Au, Pd, Pt) and their (100) and (111) facets, we explore various adsorption sites, orientations, and distances. Sobol sensitivity analysis identifies the most influential parameters for each system, which are then optimized to minimize errors in adsorption energies. This targeted optimization yields substantial accuracy gains, reducing root-mean-square errors by approximately 20-60%. The modified method provides reliable predictions for catalytic studies where the default parameterization can fail qualitatively. However, such improvements come at the cost of reduced transferability across systems and properties, emphasizing that parameter optimization must be carefully tailored to the specific chemical context.