The Young Investigator Network is the platform and democratic representation of interests for independent junior research group leaders and junior professors at the Karlsruhe Institut of Technology.

News

Visit the News Archive 2021 to learn what YIN members have recently achieved.

grainYIN
Rediscovering ancient grain varieties for the sustainable production of organic food

May ancient grain varieties grown organically and artisanal baked improve digestive tolerance? In the project ReBIOscover funded by the Federal Ministry of Food and Agriculture, Katharina Scherf and her colleagues want to find the answer. While cereals are among the most important nutrient suppliers worldwide, they also contain gluten and other immunoreactive components. Though only 4 % of Germans are diagnosed with dietary intolerance, about 20 % try to avoid cereals due to health issues. The main causes might be differences in highly bred cultivars and changed manufacturing procedures as many consumers report better tolerance of artisanal baked goods.

project discription
Perowskit-Tandem-Solarmodul
Open source tool simulates energy yield of complex photovoltaic modules

Perovskite-based tandem solar cells are already achieving record efficiencies of up to 29.5 percent in the laboratory. However, the novel component architectures make the calculation of energy yield more complex. Ulrich W. Paetzold and his team have, therefore, developed an Energy Yield Calculator (EYcalc). This software uses real irradiation data and takes variable parameters into account, such as location, installation angle and the position of the sun, in order to determine the annual energy yield with hourly resolution. The open-source tool is suitable for all conventional, but above all also for highly complex solar cell architectures.

EYcalc on GitHub
Neuronale Netze ermöglichen präzise Materialsimulationen – bis hinunter auf die Ebene einzelner AtomePascal Friederich, KIT
Nature Materials: Machine learning accelerates material simulations

Research, development, and production of new materials depend on fast and at the same time accurate simulation methods. For this, a high degree of precision over various time and size scales – from the atom to the material – with limited computational effort is essential. "Compared to conventional methods based on classical or quantum mechanical calculations, a significant speed advantage can be achieved with neural networks specifically tailored to material simulations," says first author Pascal Friederich. The combination of machine learning and molecular mechanics methods could speed up material simulations even further in the future.

press info

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