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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Disentangling the impact of packing in colloidal a
 nd molecular self-assembly - Rose Cersonsky (Unive
 rsity of Wisconsin-Madison)
DTSTART;TZID=Europe/London:20230822T113000
DTEND;TZID=Europe/London:20230822T120000
UID:TALK202582AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/202582
DESCRIPTION:Geometric packing is an oft-used causal mechanism 
 for structure formation acrossmany length scales &
 ndash\; from the entropic ordering of colloidal na
 noparticles to molecularco-crystallization of drug
 -like molecules. However\, its exact role is hard-
 to-quantify\,particularly in regimes where many co
 mpeting forces can motivate nucleation\, andeviden
 tly\, crystallization. In this talk\, I will first
  discuss the impact of geometric packing insystems
  where its effect should be most pronounced &ndash
 \; hard\, faceted nanoparticles thatself-assemble 
 based on volume exclusion alone. Using Maxwell rel
 ations\, I will showthat markers for &ldquo\;packi
 ng&rdquo\; behavior are absent in the regimes wher
 e self-assemblyoccurs\, pointing to packing as a c
 orrelative\, rather than causal\, force in the eme
 rgenceof spontaneous order. I will then shift focu
 s to crystallization in small-molecule systems\,wh
 ere the role of packing is still an open question 
 and hard to pinpoint in analyses.Using physics-bas
 ed machine learning representations and hybridsupe
 rvised-unsupervised models\, I show how we can ide
 ntify the role of enthalpic andgeometric component
 s in stabilizing (or destabilizing) these systems.
  I will end with anoutlook on the future of physic
 s-informed machine learning for understanding mole
 cularpacking\, including future work directions.Th
 is talk will pull from the following works:● Cerso
 nsky\, R. K.\, Pakhnova\, M.\, Engel\, E. A.\, Cer
 iotti\, M.\, A data-driveninterpretation of the st
 ability of organic molecular crystals. Chemical Sc
 ience 14\,1272&ndash\;1285.● Helfrecht\, B. A.\, C
 ersonsky\, R. K.\, Fraux\, G.\, Ceriotti\, M.\, St
 ructure-propertymaps with Kernel principal covaria
 tes regression. Machine Learning: Scienceand Techn
 ology 1\, 045021.● Cersonsky\, R. K.\, Van Anders\
 , G.\, Dodd\, P. M.\, Glotzer\, S. C.\, Relevance 
 ofpacking to colloidal self-assembly. Proceedings 
 of the National Academy ofSciences● 115\, 1439&nda
 sh\;1444
LOCATION:External
CONTACT:
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