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BEGIN:VEVENT
DTSTAMP:20220812T074334Z
LOCATION:Nairobi Room
DTSTART;TZID=Europe/Stockholm:20220628T123000
DTEND;TZID=Europe/Stockholm:20220628T130000
UID:submissions.pasc-conference.org_PASC22_sess143_msa269@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 combin
 ing 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 contai
 ned both in microscopic simulation and experimental measurements.\n\nDomai
 n: Chemistry and Materials, Physics
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