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SUMMARY:Automated Identification of Collective Variables for Polymeric sys
 tems from Molecular Dynamics Data - Atreyee Banerjee\, MPI for Polymer Res
 earch
DTSTART:20220314T140000Z
DTEND:20220314T143000Z
UID:TALK167303@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION:Syndiotatic polysterene (sPS) exhibits complex polymorphic beh
 aviour\, resulting in rugged free energy landscapes (FELs). In these FELs\
 , the high energy barriers that separate different polymorph basins hinder
  their systematic exploration by traditional molecular dynamics simulation
 s. Enhanced sampling methods have the potential remedy this problem with p
 rior knowledge of collective variables (CVs) that can resolve the relevant
  transition pathways\, typically identified through physical or chemical e
 xpertise. Recently\, data-driven methods have attracted considerable atten
 tion for learning the CVs without significant a priori insight. We aim to 
 use different dimensionality reduction methods varying from linear methods
  like principal component analysis to more complex non-linear methods like
  uniform manifold approximation and projection for comparing the low dimen
 sional embedding. In order to efficiently describe the local environment o
 f sPS monomers\, we adapt an atomic representation used in machine learnin
 g. One of the advantages of using these descriptors is that they do not re
 quire the incorporation of excessive system-specific intuition and demonst
 rate good transferability properties. Recently we applied these data drive
 n methods to predict the glass transition temperatures for polymer melts.\
 n
LOCATION:Venue to be confirmed
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