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

Area of Science:

• Computational Chemistry
• Materials Science
• Surface Science

Background:

• Organic molecules spontaneously form ordered superstructures on nonreactive surfaces.
• Accurate modeling of these self-assembled systems often relies on computationally intensive quantum mechanical methods like Density Functional Theory (DFT).
• DFT calculations are impractical for large systems (hundreds of atoms) without pre-existing approximate structures.

Purpose of the Study:

• To develop an efficient computational algorithm for predicting the structure of self-assembled organic molecules on surfaces.
• To simplify the quantum mechanical description of molecular interactions using parameterized pair potentials.
• To provide accurate initial structures for subsequent high-accuracy DFT calculations.

Main Methods:

• Developed a novel algorithm combining Monte Carlo (MC) simulation with a pair potential method.
• The MC simulation utilizes pair-parameterized forces, simplifying quantum mechanical interactions.
• The algorithm predicts molecular position and deformation to minimize system potential energy, starting from experimental superstructure periodicity.

Main Results:

• Successfully predicted molecular structures for organic pigment self-ordering on highly oriented pyrolytic graphite and Si(111)-In surfaces.
• The algorithm efficiently handles systems with hundreds of atoms by simplifying the quantum mechanical description.
• Generated structures serve as excellent starting points for more rigorous DFT analyses.

Conclusions:

• The developed MC-based algorithm offers a computationally tractable approach for modeling organic molecule self-assembly.
• This method significantly reduces the computational cost compared to direct DFT calculations for large systems.
• The algorithm facilitates the prediction of ordered molecular superstructures, aiding in materials design and discovery.