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We propose a computationally lean, two-stage approach that reliably predictsself-assembly behavior of complex charged molecules on a metallic surfacesunder electrochemical conditions. Stage one uses ab initio simulations toprovide reference data for the energies (evaluated for archetypicalconfigurations) to fit the parameters of a conceptually much simpler andcomputationally less expensive model of the molecules: classical, sphericalparticles, representing the respective atomic entities, a soft but perfectlyconductive wall potential represents the metallic surface. Stage two feeds theenergies that emerge from this classical model into highly efficient andreliable optimization techniques to identify via energy minimization theordered ground state configurations of the molecules. We demonstrate the powerof our approach by successfully reproducing, on a semi-quantitative level, theintricate supramolecular ordering observed experimentally for PQP$^+$ andClO$_4^-$ molecules at an Au(111)-electrolyte interface, including theformation of open-porous, self-hosts--guest, and stratified bilayer phases as afunction of the electric field at the solid--liquid interface. We also discussthe role of the perchlorate ions in the self-assembly process, whose positionscould not be identified in the related experimental investigations.

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