Molecular self-organization: Predicting the pattern diversity and lowest energy state of competing ordering motifs

TL;DR

This study combines experimental STM imaging and computational modeling to predict and understand the diverse self-organized patterns of flexible dendron monolayers on graphite, identifying the most stable configurations.

Contribution

It introduces a coarse-grained interaction model and Monte Carlo simulations that accurately reproduce experimental pattern diversity and predict the thermodynamically most stable pattern.

Findings

Experimental patterns include up to seven stable motifs.

Simulations successfully reproduce local and global ordering.

The most stable pattern matches experimental observations after heating.

Abstract

Self-organized monolayers of highly flexible \Frechet dendrons were deposited on graphite surfaces by solution casting. Scanning tunneling microscopy (STM) reveals an unprecedented variety of patterns with up to seven stable hierarchical ordering motifs serving as a versatile model system. The essential molecular properties determined by molecular mechanics simulations are condensed to a coarse grained interaction site model of various chain configurations. In a Monte Carlo approach with random starting configurations the experimental pattern diversity can be reproduced in all facets of the local and global ordering. Based on an energy analysis of the Monte Carlo and molecular mechanics modeling the thermodynamically most stable pattern is predicted coinciding with the pattern, which dominates in the STM images after several hours or upon moderate heating.