Predictive Registry Optimization of Molecular Adsorbates on Solid Surfaces.

Area of Science:

• Surface science
• Materials science
• Supramolecular chemistry

Background:

• Predicting large organic molecule adsorption on crystalline substrates is vital for functional surfaces in electronics and catalysis.
• Standard quantum-chemical methods struggle with the size and conformational complexity of large π-conjugated molecules.

Purpose of the Study:

• To develop a generalizable and physically intuitive method for predicting adsorption geometries of large organic molecules on surfaces.
• To overcome limitations of standard computational approaches for complex molecular systems.

Main Methods:

• A registry analysis framework maximizing overlap between surface-adsorbed molecule features ('spikes') and substrate features ('pockets').
• Utilized a Monte Carlo-like approach inspired by the alkane-on-graphite model.
• Applied registry scoring to Scanning Tunneling Microscopy (STM) data.

Main Results:

• The method accurately assigns adsorption geometries for structurally complex molecules without requiring costly computations.
• Demonstrated high-resolution determination of molecular arrangements on surfaces.
• Successfully determined the absolute conformation of atropisomers using geometric registry.

Conclusions:

• Geometric registry analysis provides a powerful and computationally efficient tool for predicting large-molecule arrangements on surfaces.
• This approach is applicable to diverse complex molecular architectures and experimental data.
• Enables precise control over surface functionalization for advanced applications.