AA2 – Materials, Light, Devices



Sparse Deep Neuronal Networks for the Design of Solar Energy Materials

Project Heads

Andrea Walther, Stephan Schmidt, Bernd Rech, Eva Unger

Project Members


Project Duration

3 years

Located at

HU Berlin


The design of new materials for solar cells still relies heavily on very time-consuming material screenings. A simulation-based approach is capable of classifying compounds of Perovskite type with respect to thermodynamic stability in the formation and further properties using density function theory [1]. Likewise, the relationship between key performance indicators can be assessed from experimental data [2]. A major challenge for the fast and reliable design of new solar energy materials is the fact that there are still significant discrepancies between the compound properties predicted by simulation and the experimental data for numerous Perovskite type materials. This motivates the development of new mathematical techniques for improved machine learning approaches targeted within this project.


Project partner:

Thomas Kühne, Dynamics of Condensed Matter, Universität Paderborn

Project Webpages


[1] R. Raghupathy, H. Wiebeler, T. Kühne, C. Felser, and H. Mirhosseini. Database screening of ternary chalcogenides for p-type transparent conductors. Chemistry of Materials, 30(19):6794–6800, 2018.

[2] E. Unger, L. Kegelmann, K. Suchan, D. Sörell, L. Korte, and S. Albrecht. Roadmap and roadblocks for the band gap tunability of metal halide perovskites. Journal of Materials Chemistry A, 5:11401–11409, 2017.

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