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BEGIN:VEVENT
DTSTAMP:20210916T132449Z
LOCATION:Henry Dunant
DTSTART;TZID=Europe/Stockholm:20210706T140000
DTEND;TZID=Europe/Stockholm:20210706T143000
UID:submissions.pasc-conference.org_PASC21_sess136_msa348@linklings.com
SUMMARY:Designing Molecular Models by Machine Learning and Experimental Da
 ta
DESCRIPTION:Minisymposium\n\nDesigning Molecular Models by Machine Learnin
 g and Experimental Data\n\nClementi\n\nThe last years have seen an immense
  increase in high-throughput and high-resolution technologies for experime
 ntal observation as well as high-performance techniques to simulate molecu
 lar systems at a microscopic level, resulting in vast and ever-increasing 
 amounts of high-dimensional data. However, experiments provide only a part
 ial view of macromolecular processes and are limited in their temporal and
  spatial resolution. On the other hand, atomistic simulations are still no
 t able to sample the conformation space of large complexes, thus leaving s
 ignificant gaps in our ability to study molecular processes at a biologica
 lly relevant scale. We present our efforts to bridge these gaps, by exploi
 ting the available data and using state-of-the-art machine-learning method
 s to design optimal coarse models for complex macromolecular systems. We s
 how that it is possible to define simplified molecular models to reproduce
  the essential information contained both in microscopic simulation and ex
 perimental measurements.\n\nDomain: Chemistry and Materials, Physics, Life
  Sciences, Engineering
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