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SUMMARY:Topology\, Molecular Simulation\, and Machine Learning as Routes t
 o Exploring Structure\, Dynamics\, and Phase Behavior in Atomic and Molecu
 lar Crystals - Professor Mark Tuckerman\, New York University
DTSTART:20230308T143000Z
DTEND:20230308T153000Z
UID:TALK193444@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:The different solid structures or polymorphs of atomic and mol
 ecular crystals often possess different physical and chemical properties. 
  Structural differences between organic molecular crystal polymorphs can a
 ffect\, for example\, bioavailability of active pharmaceutical formulation
 s\, the lethality of contact insecticides\, and diffusive behavior in host
 -guest systems.  In metallic crystals\, structural differences may determi
 ne how different phases may be used in electronic device applications.  Cr
 ystallization conditions can influence polymorph selection\, making an exp
 erimentally driven hunt for polymorphs difficult.  These efforts are furth
 er complicated when polymorphs initially obtained under a particular exper
 imental protocol “disappear” in favor of another polymorph in subseque
 nt repetitions of the experiment.  Theory and computation can potentially 
 play a vital role in mapping the landscape of crystal polymorphism.  Tradi
 tional methods for predicting crystal structures and investigating solid-s
 olid phase transformation behavior face their own challenges\, and therefo
 re\, new approaches are needed.  In this talk\, I will show\, by leveragin
 g concepts from mathematics\, specifically geometry and topology\, and cla
 ssical and quantum statistical mechanics in combination with techniques of
  molecular simulation\, and machine learning\, that new paradigms are emer
 ging in our ability to predict molecular crystal structures and determine 
 kinetics of polymorphic phase transformations and guest-molecule diffusion
 .
LOCATION:Unilever Lecture Theatre\, Yusuf Hamied Department of Chemistry
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