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SUMMARY:Topology\, Molecular Simulation\, and Machine Learning as Routes t
 o Exploring Structure and Phase Behavior in Molecular and Atomic Crystals 
 - Mark Tuckerman\, NYU
DTSTART:20201112T140000Z
DTEND:20201112T150000Z
UID:TALK153097@talks.cam.ac.uk
CONTACT:Jan Behrends
DESCRIPTION:Organic molecular crystals frequently exist in multiple forms 
 known as polymorphs. Structural differences between crystal polymorphs can
  affect desired properties\, such as bioavailability of active pharmaceuti
 cal formulations\, lethality of pesticides\, or electrical conductivity of
  organic semiconductors. Crystallization conditions can influence polymorp
 h selection\, making an experimentally driven hunt for polymorphs difficul
 t. Such efforts are further complicated when polymorphs initially obtained
  under a particular experimental protocol “disappear” in favor of anot
 her polymorph in subsequent repetitions of the experiment. Consequently\, 
 theory and computation can potentially play a vital role in mapping the la
 ndscape of crystal polymorphism. Traditional crystal structure prediction 
 methods face their own challenges\, and therefore\, new approaches are nee
 ded. In this talk\, I will show\, by leveraging concepts from mathematics\
 , specifically geometry and topology\, and statistical mechanics in combin
 ation with techniques of molecular simulation\, traditional methods\, and 
 machine learning\, that a new paradigm in crystal structure prediction may
  be emerging. Examples demonstrating prediction of structures of crystals\
 , co-crystals\, and phase transitions will be presented.
LOCATION:Details of video conferencing will be distributed nearer the time
 .
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