By Markus Meuwly, Department of Chemistry, University of Basel, Switzerland
Machine Learning-based techniques have the potential to fundamentally change how we approach characterizing and understanding chemical and biological materials. It is anticipated that ML applied to protein dynamics allows to carry out atomistic simulations on functionally relevant time scales using models approaching chemical accuracy. In the present seminar I will present methodological advances and their application to position-dependent spectroscopy and ligand (re-)binding reactions in small proteins with a focus on the links between simulations and experiment. Both, opportunities and limitations of machine learning-based techniques will also be discussed.
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