Coarse-graining classical and quantum systems

Cecilia Clementi
Freie Universität Berlin

The last years have seen an immense increase in high-throughput and high-resolution technologies for experimental observation as well as high-performance techniques to simulate molecular systems at a microscopic level, resulting in vast and ever-increasing amounts of high-dimensional data. However, experiments provide only a partial view of the molecular processes and are limited in their temporal and spatial resolution. On the other hand, simulations are still not able to completely characterize large and/or complex molecular processes over long timescales, thus leaving significant gaps in our ability to study these processes at a physically relevant scale. We present our efforts to bridge these gaps, by combining statistical physics with state-of-the-art machine-learning methods to design optimal coarse models for complex macromolecular systems. We derive simplified molecular models to reproduce the essential information contained both in microscopic simulation and experimental measurements.

Presentation (PDF File)

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