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SUMMARY:Modeling shape transformations in liquid crystal elastomers: a mac
 hine learning approach to inverse design - Robin Selinger (Kent State Univ
 ersity)
DTSTART:20230815T090000Z
DTEND:20230815T100000Z
UID:TALK203992@talks.cam.ac.uk
DESCRIPTION:Liquid Crystal Elastomers (LCE) are stimuli-responsive\, progr
 ammable actuators that undergo shape-morphing in response to a change of t
 emperature\, illumination\, or other environmental cues. The resulting act
 uation trajectory is programmed by patterning the nematic director field\,
  e.g. by forming the sample between glass substrates with prescribed surfa
 ce anchoring patterns which may be identical or entirely different. Using 
 a GPU-based finite element simulation developed in-house\, we explore mech
 anisms by which arrays of topological defects in the microstructure of LCE
  thin coatings give rise to complex transformations in surface topography.
  We also develop a machine learning algorithm to optimize the shape of res
 ulting topological features.\nIn separate work\, we describe our recent di
 scovery that the Frank-Read source mechanism\, which drives emission of co
 ncentric dislocation loops in crystalline solids\, can likewise drive emis
 sion of disclination loops in a nematic liquid crystal. We discuss potenti
 al implications for controlling microstructural evolution in passive and a
 ctive nematic liquid crystals.\nCoauthors part 1: Youssef Mosaddeghian Gol
 estani\, Michael Varga\, Badel Mbanga\nCoauthors part 2: Cheng Long\, Jona
 than Selinger\, Matthew Deutsch\n&nbsp\;\n&nbsp\;\n&nbsp\;
LOCATION:Seminar Room 2\, Newton Institute
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