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SUMMARY:Pushing the frontiers of molecular dynamics simulations
DTSTART;VALUE=DATE:20241007
DTSTAMP:20260526T102522Z
UID:656c0b05b4eb2668cdac346070263fe0b02803486de7118002e5823c
CATEGORIES:Conferences - Seminars
DESCRIPTION:General information\nThis workshop is part of the MDDB projec
 t.\nOnsite registrations (which enables attendance to the social activitie
 s and presentation of a poster) are closed for this workshop\, however the
  EPFL community is always welcome to listen to some talks that are of inte
 rest for their research. Online attendance is also available\, but regist
 ration is mandatory (Deadline: September 30th 2024\, midnight CEST) throug
 h the event website (active CECAM account mandatory).\n\nDescription\nIn 
 only a few decades the Molecular Dynamics (MD) world has moved from a fiel
 d dominated by a few highly specialized groups with a deep knowledge of th
 e technology\, who are typically method and software developers\, to a sit
 uation where MD is present in many more areas of science\, including biolo
 gy. Molecular mechanics is used to relax models e.g. in AlphaFold\, a numb
 er of experimental techniques like Cryo-EM and NMR now regularly combine t
 heir data with simulations\, and we are seeing an emergence of data-driven
  modeling where huge amounts of experimental data e.g. from mutation studi
 es or genome sequencing are combined with simulations (not least during th
 e Covid-19 pandemic). On the one hand\, the field has seen tremendous prog
 ress with much more accurate force-fields\, the development of more effici
 ent MD engines\, better understanding of enhanced sampling algorithms – 
 not to mention advances in computers and custom-designed hardware that hav
 e transformed MD in a technique with predictive power\, which is used exte
 nsively to decipher the molecular mechanisms of life.\nHowever\, while the
  field is thriving\, we are also faced with numerous challenges: Exascale 
 computers will provide more power than ever before\, but it will not be po
 ssible to use all that power in simulations without advances in sampling a
 lgorithms. Classical force fields are arguably reaching their limits\, and
  with commodity hardware increasingly optimized for AI workloads\, it is a
 rguably time to fundamentally revisit our approaches to force fields – b
 ut currently those approaches fall orders-of-magnitude short of classical 
 simulations when it comes to simulation length\, which brings us back to t
 he sampling efficiency challenge. In parallel\, community efforts are coor
 dinating the use of many thousands of private computers whose combined pow
 er allows to obtain ensembles in many cases richer than those obtained wit
 h large supercomputers. Combination of MD simulations and coarse grained a
 nd mesoscopic models open new frontiers on studying small organelles of ev
 en eukaryotic chromatin\, which is proving to be an exceptionally valuable
  complement e.g. to cryo-tomography and super-resolution microscopy. Howev
 er\, these models clearly do not reach timescales where thorough sampling 
 is achieved over the entire system\; how should this be handled? Can we in
 tegrate more experimental data as restraints\, or do we need new generatio
 ns of super-coarse-grained models? Can we find ways to couple model scales
  without inherently being stuck at the timescale of the innermost/slowest 
 model?\nWe believe it is time to review recent developments\, to criticall
 y assess areas where there is potential for major scientific advances\, id
 entify bottlenecks and challenges that can be solved\, and jointly set out
  a community roadmap for key issues to work on. We want to interrogate and
  learn from world leaders in the field on:\n\n	The use of MD simulations t
 o understand the behavior of large supramolecular organisms\n	Recent impro
 vements in coarse grained and mesoscopic models\n	The most recent advances
  in ensemble techniques\n	The frontier between machine learning and molecu
 lar simulations\n	The problem of data and how to integrate the MD-field in
 to the data science paradigm\n
LOCATION:BCH2103
STATUS:CONFIRMED
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