Peptides, short chains of amino acids, can spontaneously assemble into multi-functional materials used in emerging technologies ranging from drug delivery to soft semiconductor devices. Designing these materials is challenging because scientists must identify the best amino acid sequence among enormous possibilities – much larger than one can experimentally test. Thus, there is an urgent need to understand the underlying principles that will allow for a shift from a human intuition-based to a rational design-based approach. In her project SupraModel, Prof. Julija Zavadlav will develop a novel computational framework, integrating multiscale modeling, molecular simulations, and machine learning. The framework will enable fast and accurate prediction of peptide co-assembly and unprecedented molecular insight into the process. With the obtained data, she aims to provide much-needed guidance for developing next-generation peptide-based materials.
Prof. Zavadlav studied physics at the University of Ljubljana where she received her Ph.D. in 2015 while working at the National Institute of Chemistry in Slovenia. In 2016, she joined the Computational Science and Engineering Laboratory at ETH Zurich and was awarded the ETH Postdoctoral Fellowship in 2017. In 2019, she was appointed as Assistant Professor for Multiscale Modeling of Fluid Materials at TUM.
Weitere Informationen:
Profile of Prof. Julija Zavadlav
Website of Professorship of Multiscale Modeling of Fluid Materials
TUM press release "12 ERC Starting Grants awarded to TUM researchers"