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<p>Organic molecules on nonreactive surfaces self-assemble to ordered superstructures. This problem is typically studied using quantum mechanical models, such as Density functional theory (DFT). However, the use of those models for systems with hundreds of atoms is computationally impractical if at least approximate structures are not known. Here, we demonstrate an algorithm employing a Monte Carlo simulation and a method of pair potentials - thus simplifying the quantum mechanical description by using pair-parameterized forces. We incorporate this model into a Monte Carlo simulation which, from a given approximate molecular superstructure periodicity obtained from experimental data, predicts the position and deformation of the ordered molecules to minimize the potential energy of the system. We test the algorithm on published results of self-ordering of selected pigments in different environments: 1) on highly oriented pyrolytic graphite in solution, and 2) on the Si(111)-In<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\((\sqrt{7}\times \sqrt{3})\)</EquationSource> </InlineEquation> surface prepared under ultra-high vacuum. The generated structures can be used as close-to-relaxed candidates for inputs to more accurate DFT calculations.</p>

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PairPotMCinator: a tool for fast simulations of the organic-molecular ordering on the solid surfaces using pair potentials

  • 期刊论文

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  • 发表日期：

    2026年4月28日

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  • J. Antoš,
  • P. Kocán

摘要

Organic molecules on nonreactive surfaces self-assemble to ordered superstructures. This problem is typically studied using quantum mechanical models, such as Density functional theory (DFT). However, the use of those models for systems with hundreds of atoms is computationally impractical if at least approximate structures are not known. Here, we demonstrate an algorithm employing a Monte Carlo simulation and a method of pair potentials - thus simplifying the quantum mechanical description by using pair-parameterized forces. We incorporate this model into a Monte Carlo simulation which, from a given approximate molecular superstructure periodicity obtained from experimental data, predicts the position and deformation of the ordered molecules to minimize the potential energy of the system. We test the algorithm on published results of self-ordering of selected pigments in different environments: 1) on highly oriented pyrolytic graphite in solution, and 2) on the Si(111)-In \((\sqrt{7}\times \sqrt{3})\) surface prepared under ultra-high vacuum. The generated structures can be used as close-to-relaxed candidates for inputs to more accurate DFT calculations.