Dresden 2020 – scientific programme

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O: Fachverband Oberflächenphysik

O 42: Focus Session: Innovation in Machine learning PRocEsses for Surface Science (IMPRESS)

O 42.5: Talk

Tuesday, March 17, 2020, 12:00–12:15, TRE Phy

SAMPLE: Surface structure search enabled by coarse graining and statistical learning — •Lukas Hörmann, Andreas Jeindl, Alexander T. Egger, and Oliver T. Hofmann — Institute of Solid State Physics, NAWI Graz, TU Graz, Petersgasse 16, 8010 Graz, Austria

The structure is the key information of an organic monolayer on an inorganic substrate. Virtually all properties depend on the polymorph. On top of that, monolayers often display diverse molecular arrangements in different unit cells. Determining these polymorphs from first principles poses a considerable challenge due to the large number of possible molecular arrangements.

To meet this challenge, SAMPLE[1] employs coarse-grained modeling and machine learning to efficiently map the minima of the potential energy surface of commensurate organic adlayers. Requiring only a few hundred DFT calculations of possible polymorphs, we use Bayesian linear regression to determine the parameters of a physically motivated energy model. These parameters yield meaningful physical insight and allow predicting adsorption energies for millions of possible polymorphs with high accuracy.

We demonstrate SAMPLE's capabilities on the systems of naphthalene[1] and TCNE[2,3] on coinage metals where we predict the energetically most favorable polymorphs and compare them to experiment.

[1] Hörmann et al., Computer Physics Communications 244, 143-155, 2019 [2] Scherbela et al., Phys. Rev. Materials 2, 043803, 2018 [3] Obersteiner et al., Nano Lett. 17, 4453-4460, 2017