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SUMMARY:Using Machine Learning to Deduce Molecular Weight Distribution fro
 m Rheology - Robert Elliott (University of Leeds)
DTSTART:20250911T143500Z
DTEND:20250911T144000Z
UID:TALK233353@talks.cam.ac.uk
DESCRIPTION:We present a methodology for inferring the molecular weight di
 stribution (MWD) of polydisperse linear polymers from their linear rheolog
 y using machine learning techniques. Inferring the MWD from rheology would
  traditionally be prohibited by the complexity of state-of-the-art tube mo
 dels\, but our technique bypasses this by use of a machine learning method
 ology. Our approach utilises the model of Das and Read (2023) to generate 
 large datasets of artificially produced rheology data. These data are used
  to train neural networks (NNs) to make MWD predictions from rheology meas
 urements. We present a method for transforming the rheology data\, using o
 nly the entanglement time and the plateau modulus\, into a &lsquo\;univers
 al&rsquo\; rheology space. Here\, any polymer chemistry can be readily ana
 lysed using the same approach. The NN is trained to predict the distributi
 on in the number of entanglements in a polymer melt\, and this is subseque
 ntly converted into the molecular weight distribution by knowledge of the 
 mean entanglement molecular weight. Since we are interested in predicting 
 the properties of industrially relevant polymers\, we focus on broad MWDs.
  We present predictions for a range of polymer chemistries\, including com
 mercial samples of polyethylene (HDPE and LLDPE). Good agreement with expe
 rimental gel permeation chromatography data is obtained.&nbsp\;
LOCATION:External
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