Speaker
Description
Computer simulations are a key complement to experiments in the laboratory, providing much greater details of a molecular process than can be observed experimentally. For instance, ab initio molecular dynamics simulations are often key to the understanding of the mechanism, rate and yield of chemical reactions. One current challenge is the in-depth analysis of the large amount of data produced by the simulations, in order to produce valuable insight and general trends. In the first part of my talk, I will present recent machine learning analysis tools used to extract relevant information from ab initio molecular dynamics simulations without a priori knowledge on chemical reactions. It is demonstrated that, in order to make accurate predictions, the models evidence empirical rules that are, today, part of the common chemical knowledge. This opens the way for conceptual breakthroughs in chemistry where machine analysis would provide a source of inspiration to humans. In the second part of my talk, I will show recent experimental and theoretical results on the photo-induced dynamics of an iron photosensitizer. Coherent structural dynamics in the excited state of an iron photosensitizer was observed through oscillations in the intensity of Kalpha x-ray emission spectroscopy (XES). Using multiconfigurational wavefunction calculations, we explain the origin of the unexpected sensitivity of core-to-core transitions to structural dynamics.
