Reliable computational prediction of supramolecular ordering of complex molecules under electrochemical conditions

We propose a computationally lean, two-stage approach that reliably predicts self-assembly behavior of complex charged molecules on a metallic surfaces under electrochemical conditions. Stage one uses ab initio simulations to provide reference data for the energies (evaluated for archetypical configurations) to fit the parameters of a conceptually much simpler and computationally less expensive model of the molecules: classical, spherical particles, representing the respective atomic entities, a soft but perfectly conductive wall potential represents the metallic surface. Stage two feeds the energies that emerge from this classical model into highly efficient and reliable optimization techniques to identify via energy minimization the ordered ground state configurations of the molecules. We demonstrate the power of our approach by successfully reproducing, on a semi-quantitative level, the intricate supramolecular ordering observed experimentally for PQP\(^+\) and ClO\(_4^-\) molecules at an Au(111)-electrolyte interface, including the formation of open-porous, self-hosts--guest, and stratified bilayer phases as a function of the electric field at the solid--liquid interface. We also discuss the role of the perchlorate ions in the self-assembly process, whose positions could not be identified in the related experimental investigations.