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SUMMARY:Exploration and learning of free energy landscapes of molecular cr
 ystals and oligopeptides - Professor Mark Tuckerman\, New York University
DTSTART:20170519T100000Z
DTEND:20170519T111500Z
UID:TALK72645@talks.cam.ac.uk
CONTACT:Rachel Ryder
DESCRIPTION:Theory\, computation\, and high-performance computers are play
 ing an increasingly important role in helping us understand\, design\, and
  characterize a wide range of functional materials\, chemical processes\, 
 and biomolecular/biomimetic structures.  The synergy of computation and ex
 periment is fueling a powerful approach to address some of the most challe
 nging scientific problems.  In this talk\, I will describe the efforts we 
 are making in my group to develop new computational methodologies that add
 ress specific challenges in free energy exploration and generation.  In pa
 rticular\, I will describe our recent development of enhanced free energy 
 based methodologies for predicting structure\, polymorphism\, and defects 
 in atomic and molecular crystals\, for exploring first-order phase transit
 ions\, and for determining conformational equilibria of oligopeptides.  Th
 e strategies we are pursuing include large time-step molecular dynamics al
 gorithms\, heterogeneous multiscale modeling and learning techniques\, whi
 ch allow “landmark” locations (minima and saddles) on a high-dimension
 al free energy surface to be mapped out\, and temperature-accelerated meth
 ods\, which allow relative free energies of the landmarks to be generated 
 efficiently and reliably.  I will then discuss new schemes for using machi
 ne learning techniques to represent and perform computations using multidi
 mensional free energy surfaces.  Finally\, if time permits\, I will descri
 be the use of machine learning techniques to enhance the accuracy and effi
 ciency of density functional theory calculations based on density learning
  models.
LOCATION:Maxwell Centre\, JJ Thomson Seminar Room
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