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DTSTAMP:20260425T051650Z
UID:1699266600@ist.ac.at
DTSTART:20231106T113000
DTEND:20231106T123000
DESCRIPTION:Speaker: Mark Tuckerman\nhosted by Bingqing Cheng\nAbstract: Th
 e different solid structures or polymorphs of atomic and molecular crystal
 s often possess different physical and chemical properties. Structural dif
 ferences between organic molecular crystal polymorphs can affect\, for exa
 mple\, bioavailability of active pharmaceutical formulations\, the lethali
 ty of contact insecticides\, and diffusive behavior in host-guest systems.
  In metallic crystals\, structural differences may determine how different
  phases may be used in electronic device applications. Crystallization con
 ditions can influence polymorph selection\, making an experimentally drive
 n hunt for polymorphs difficult. These efforts are further complicated whe
 n polymorphs initially obtained under a particular experimental protocol 
 “disappear” in favor of another polymorph in subsequent repetitions of
  the experiment. Theory and computation can potentially play a vital role 
 in mapping the landscape of crystal polymorphism. Traditional force-field 
 based methods for predicting crystal structures and investigating solid-so
 lid phase transformation behavior face their own challenges\, and therefor
 e\, new approaches are needed. In this talk\, I will show\, by leveraging 
 concepts from mathematics\, specifically geometry\, topology\, and machine
  learning\, a force-field free method for predicting molecular crystal str
 uctures is possible. The new approach yields predictions of structures at 
 least an order of magnitude faster than traditional energybased methods. O
 nce a polymorph landscape is obtained\, techniques of molecular simulation
  and machine learning can be used to predict the kinetics of structural ph
 ase transitions between different polymorphs. In this way\, I hope to pres
 ent a convincing case that new paradigms are emerging in our ability to pr
 edict molecular crystal structures and determine kinetics of polymorphic p
 hase transformations.
LOCATION:Raiffeisen Lecture Hall\, ISTA
ORGANIZER:arinya.eller@ist.ac.at
SUMMARY:Mark Tuckerman: Topology\, molecular simulation\, and machine learn
 ing as routes to exploring structure\,  dynamics\, and phase behavior in a
 tomic and molecular crystals
URL:https://talks-calendar.ista.ac.at/events/4222
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