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DTSTAMP:20210916T132450Z
LOCATION:Henry Dunant
DTSTART;TZID=Europe/Stockholm:20210706T150000
DTEND;TZID=Europe/Stockholm:20210706T153000
UID:submissions.pasc-conference.org_PASC21_sess136_msa359@linklings.com
SUMMARY:Markov State Model Based Adaptive Sampling Algorithms
DESCRIPTION:Minisymposium\n\nMarkov State Model Based Adaptive Sampling Al
 gorithms\n\nShukla\n\nOne of the key limitations of Molecular Dynamics (MD
 ) simulations is the computational intractability of sampling protein 
 ;conformational landscapes associated with either large system size or lon
 g time scales. To overcome this bottleneck, Markov State Model based adapt
 ive sampling algorithms have been introduced. In this talk, I will focus o
 n recent developed REinforcement learning based Adaptive samPling (RE
 AP) algorithm that aims to efficiently sample conformational space by lear
 ning the relative importance of each order parameter as it samples th
 e landscape. To achieve this, the algorithm uses concepts from the field&n
 bsp;of reinforcement learning, a subset of machine learning, which rewards
  sampling along important degrees of freedom and disregards others th
 at do not facilitate exploration or exploitation. \n\nDomain: Chemist
 ry and Materials, Physics, Life Sciences, Engineering
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