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DTSTART:20210328T030000
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DTSTAMP:20260404T015602Z
UID:609578825c747773469322@ist.ac.at
DTSTART:20210517T140000
DTEND:20210517T150000
DESCRIPTION:Speaker: Carl Goodrich\nAbstract: The inverse problem of design
 ing component interactions to target emergent structure is fundamental to 
 numerous applications in biotechnology\, materials science\, and statistic
 al physics. Equally important is the inverse problem of designing emergent
  kinetics\, but this has received considerably less attention. Using recen
 t advances in automatic differentiation\, I will show how kinetic pathways
  can be precisely designed by directly differentiating through statistical
 -physics models\, namely free energy calculations and molecular dynamics s
 imulations. We will consider a few systems that are crucial to our underst
 anding of structural self-assembly\, including bulk crystallization and sm
 all nanoclusters\, where we are able to assemble precise dynamical feature
 s using gradient information. Moreover\, we use this approach to learn non
 -trivial features about this new\, high-dimensional design space\, allowin
 g us to accurately predict when multiple kinetic features can be simultane
 ously and independently controlled. These results provide a concrete and g
 eneralizable foundation for studying non-structural self-assembly\, includ
 ing kinetic properties as well as other complex emergent properties\, in a
  vast array of systems.
LOCATION:Online Event ()\, ISTA
ORGANIZER:kkhudiak@ist.ac.at
SUMMARY:Carl Goodrich: Self-assembling kinetics: accessing a new design spa
 ce with differentiable statistical-physics models
URL:https://talks-calendar.ista.ac.at/events/3195
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