TU Graz
Graz University of Technology

Advanced Materials Science

The Field-of-Expertise Advanced Materials Science is an interdisciplinary network of researchers at the TU Graz in chemistry, physics, architecture, mechanical engineering, civil engineering, electrical engineering and geodesy who discover, characterize and model materials, functional coatings and components. Thirty-three institutes from six faculties are presently involved.

Advanced Materials Day is the yearly meeting where the members of the FOE present their latest results. In 2019, the meeting will be held on September 26 at the Neue Technik campus in HS H "Ulrich Santner" in the Chemistry building.



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Tomas Kamencek

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Stefan Cesnik

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Johannes Cartus

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Andreas Jeindl

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Fabio Calcinelli

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thomas taucher

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Anna Werkovits

06:00 - 06:00

Stability of lead-based perovskite solar cells with respect to structural changes
Lisanne Demelius

Abstract: Perovskite solar cells are a rising star in the field of thin film photovoltaics. Their efficiency increased to over 25% within hardly more than a decade and has thus reached values comparable to those of crystalline silicon solar cells. The success of perovskite solar cells lies in the rare combination of easy, low-temperature processability and excellent photovoltaic properties. However, serious challenges are faced when it comes to their long-term stability. Degradation can be induced by almost all external factors, such as light, oxygen or humidity. The degradation mechanisms can be complex and are not yet fully understood. This thesis' work takes a look at the response of perovskite solar cells to several different external stressors, most importantly light, and investigates the role of the hole transport layer. The stability behaviour under illumination is investigated for an organic and an inorganic hole transport layer.

06:00 - 06:00

SAMPLE: Surface structure search enabled by coarse graining and statistical learning
Lukas Hoermann

Abstract: Studying the electronic structure of organic monolayers on inorganic substrates requires knowledge about their atomistic structure. Such monolayers often display rich polymorphism arising from diverse molecular arrangements in different unit cells. The large number of possible arrangements poses a considerable challenge for determining the different polymorphs from first principles.

To meet this challenge, we developed SAMPLE[1-3], which 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 as input, 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 SAMPLEs 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 experimental data.

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


Posters 15:30 - 17:30

Anna Maria Coclite, Gregor Trimmel

1 posters.