Organic thin films exhibit a large structural variability, and many of their properties depend strongly on molecular arrangement. Which polymorph a thin film forms depends on a variety of factors, including the fabrication conditions and the properties of the substrate on which the film grows. Predicting which structure a thin film will assume on a substrate is impossible through traditional first-principle modeling alone, because of the combinatorial explosion in the number of possible polymorphs. Employing smart-data machine learning, we can now perform extensive layer-by-layer structure searches at organic/inorganic interfaces on the basis of only a few hundred first-principles calculations. Here, we use this method to study the first two layers of benzoquinone on two different substrates, Ag(111) and graphene, identifying the energetically most favorable structures on both substrates and studying their electronic properties. Our results indicate that for the first layer, similar structures are favorable on both substrates, while for the second layer we find different structures. Interestingly, graphene favors one, while Ag favors the other. We explain this switch in stability as an effect of the interplay between the different interlayer electronic coupling of the two structures and the different charge transfer on the two substrates.

Substrate-induced charge transfer influencing multilayer growth: Insights from DFT and machine learning

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.