Dimerization of dehydrogenated polycyclic aromatic hydrocarbons on graphene

TL;DR

This study explores the chemical dimerization of dehydrogenated polycyclic aromatic hydrocarbons on graphene using an innovative combination of evolutionary algorithms and machine learning to identify stable structures and understand formation mechanisms.

Contribution

The paper introduces a novel computational approach combining evolutionary algorithms and machine learning to investigate PAH dimerization on graphene, revealing stable structures and mechanisms.

Findings

Identified stable dimer structures with energies matching experimental data.

Elucidated the mechanism of coronene dimer formation on graphene.

Demonstrated the effectiveness of machine learning-augmented structure searches.

Abstract

Dimerization of polycyclic aromatic hydrocarbons (PAHs) is an important, yet poorly understood, step in the on-surface synthesis of graphene (nanoribbon), soot formation, and growth of carbonaceous dust grains in the interstellar medium (ISM). The on-surface synthesis of graphene and the growth of carbonaceous dust grains in the ISM require the chemical dimerization in which chemical bonds are formed between PAH monomers. An accurate and cheap method of exploring structure rearrangements is needed to reveal the mechanism of chemical dimerization on surfaces. This work has investigated the chemical dimerization of two dehydrogenated PAHs (coronene and pentacene) on graphene via an evolutionary algorithm augmented by machine learning surrogate potentials and a set of customized structure operators. Different dimer structures on surfaces have been successfully located by our structure search methods. Their binding energies are within the experimental errors of temperature programmed desorption measurements. The mechanism of coronene dimer formation on graphene is further studied and discussed.