DescriptionThe properties of a material depend on its structure, and no
theoretical prediction of the most stable thin film structures through
traditional, exhaustive first-principle studies is feasible due to the
combinatorial explosion in the number of possible polymorphs.
For monolayers, the machine-learning based SAMPLE approach  can
already circumvent this problem, by using a few hundred DFT calculations to
evaluate the energy of millions of possible polymorphs through Bayesian
Linear Regression. It is our intention to extend the applicability of SAMPLE
from monolayers to (meta)stable thin films.
Assuming a Frank-van der Merwe growth mechanism is at play, we can
investigate thin-film structures by considering consecutive monolayers
forming on top of one another.
As a first step, we study the formation of a second layer on top of the most
stable monolayer predicted by SAMPLE for a well known system,
Benzoquinone on Ag(111).
We evaluate the ways in which the electronic properties of the substrate can
promote the formation of different second layers, and we give an assessment
of the impact of such a change on the layer-to-layer charge transport rate in
the thin film, as one can predict within the hopping regime.
|Period||18 Mar 2021|
|Event title||APS March Meeting 2021|
|Location||Virtual, United States|
|Degree of Recognition||International|