Photosynthetic Antennas in a Computational Microscope: Training a new generation of computational scientists

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Photosynthesis relies on harvesting the sun light and on transforming the solar energy into chemical energy to sustain almost all life on earth. An enhanced molecular-level understanding of photosynthesis and particularly of the light-harvesting process is of key significance: Firstly, a molecular-level understanding of solar light harvesting and how plants and other organisms achieve this is important if we want to figure out the working principles of nature. From this, we can learn design principles of (organic) solar cells. Secondly, controlling the downregulation of photosynthesis is seen as a strategy for the optimization of crop productivity especially by means of novel tools and biotechnological solutions. In the Doctoral Network “Photosynthetic Antennas in a Computational Microscope” we aim at training a new generation of computational scientists which can treat complex and interdisciplinary problems such as light harvesting on a molecular level using theoretical and computational tools. The interdisciplinary nature of the problem requires a combined knowledge from biology, chemistry, physics and computer science in order to combine state-of-the-art approaches like molecular dynamics simulations, quantum chemistry, theoretical spectroscopy and machine learning into multi-scale schemes. This joint undertaking is a unique chance in research but especially also in training young scientists in interdisciplinary teamwork, method training and high-performance computing in academic as well as non-academic settings. Aim of the Doctoral Network is a detailed molecular understanding of light harvesting from the computational point of view and especially of the downregulatory mechanisms of photosynthesis present in higher plants and diatoms. While the undertaking exclusively focuses on theoretical and computational approaches, the calculation of spectroscopic properties for a direct comparison to experimental findings is of key importance.