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VERSION:2.0
PRODID:-//Memento EPFL//
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SUMMARY:CECAM workshop: " Pushing the frontiers of molecular dynamics simu
 lations"
DTSTART;VALUE=DATE:20241007
DTSTAMP:20260417T150155Z
UID:9716280e0468aa08e06eeabb31313b002a988c202005b543b783ceae
CATEGORIES:Conferences - Seminars
DESCRIPTION:You can apply to participate and find all the relevant informa
 tion (speakers\, abstracts\, program\,...) on the event website: https://
 www.cecam.org/workshop-details/pushing-the-frontiers-of-molecular-dynamics
 -simulations-1275\n.\n\nRegistration is required to attend the full event\
 , take part in the social activities and present a poster at the poster se
 ssion (if any).  However\, the EPFL community is welcome to attend sp
 ecific lectures without registration if the topic is of interest to their
  research. Do not hesitate to contact the CECAM Event Manager if you hav
 e any question.\n\nDescription\nThis workshop is part of the MDDB project
 .\nIn only a few decades the Molecular Dynamics (MD) world has moved from 
 a field dominated by a few highly specialized groups with a deep knowledge
  of the technology\, who are typically method and software developers\, to
  a situation where MD is present in many more areas of science\, including
  biology. Molecular mechanics is used to relax models e.g. in AlphaFold\, 
 a number of experimental techniques like Cryo-EM and NMR now regularly com
 bine their data with simulations\, and we are seeing an emergence of data-
 driven modeling where huge amounts of experimental data e.g. from mutation
  studies or genome sequencing are combined with simulations (not least dur
 ing the Covid-19 pandemic). On the one hand\, the field has seen tremendou
 s progress with much more accurate force-fields\, the development of more 
 efficient MD engines\, better understanding of enhanced sampling algorithm
 s – not to mention advances in computers and custom-designed hardware th
 at have transformed MD in a technique with predictive power\, which is use
 d extensively to decipher the molecular mechanisms of life.\nHowever\, whi
 le the field is thriving\, we are also faced with numerous challenges: Exa
 scale computers will provide more power than ever before\, but it will not
  be possible to use all that power in simulations without advances in samp
 ling algorithms. Classical force fields are arguably reaching their limits
 \, and with commodity hardware increasingly optimized for AI workloads\, i
 t is arguably time to fundamentally revisit our approaches to force fields
  – but currently those approaches fall orders-of-magnitude short of clas
 sical simulations when it comes to simulation length\, which brings us bac
 k to the sampling efficiency challenge. In parallel\, community efforts ar
 e coordinating the use of many thousands of private computers whose combin
 ed power allows to obtain ensembles in many cases richer than those obtain
 ed with large supercomputers. Combination of MD simulations and coarse gra
 ined and mesoscopic models open new frontiers on studying small organelles
  of even eukaryotic chromatin\, which is proving to be an exceptionally va
 luable complement e.g. to cryo-tomography and super-resolution microscopy.
  However\, these models clearly do not reach timescales where thorough sam
 pling is achieved over the entire system\; how should this be handled? Can
  we integrate more experimental data as restraints\, or do we need new gen
 erations of super-coarse-grained models? Can we find ways to couple model 
 scales without inherently being stuck at the timescale of the innermost/sl
 owest model?\nWe believe it is time to review recent developments\, to cri
 tically assess areas where there is potential for major scientific advance
 s\, identify bottlenecks and challenges that can be solved\, and jointly s
 et out a community roadmap for key issues to work on. We want to interroga
 te and learn from world leaders in the field on:\n\n	The use of MD simulat
 ions to understand the behavior of large supramolecular organisms\n	Recent
  improvements in coarse grained and mesoscopic models\n	The most recent ad
 vances in ensemble techniques\n	The frontier between machine learning and 
 molecular simulations\n	The problem of data and how to integrate the MD-fi
 eld into the data science paradigm\n\nReferences\n[1] M. Zimmerman\, J. Po
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  J. Coffland\, E. Fadda\, V. Voelz\, J. Chodera\, G. Bowman\, Nat. Chem.\,
  13\, 651-659 (2021)\n[2] A. Glielmo\, B. Husic\, A. Rodriguez\, C. Cleme
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 ández-Alvarez\, S. Shinjo\, M. Wieczor\, S. Ivanova\, O. Martins de Brito
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LOCATION:BCH 2103 https://plan.epfl.ch/?room==BCH%202103
STATUS:CONFIRMED
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