Predictive Registry Optimization of Molecular Adsorbates on Solid Surfaces

Understanding and predicting how large organic molecules adsorb on crystalline substrates is essential for designing functional surfaces in electronics, catalysis, and supramolecular chemistry. Standard quantum-chemical approaches often fail for large π-conjugated molecules due to size and conformational diversity. We present a physically intuitive, generalizable registry analysis that overcomes this by maximizing the overlap between surface-oriented hydrogen atoms ("spikes") and the periodic graphite hexagon centers ("pockets") in a Monte Carlo-like approach. Inspired by the alkane-on-graphite model, the framework extends to structurally complex architectures to assign adsorption geometries with high resolution, without costly computations. Registry scoring applied to STM data enabled determining the absolute conformation of atropisomers, demonstrating geometric registry as an applicable measure for predicting large-molecule arrangements on surfaces.