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SUMMARY:Exploration and learning of free energy landscapes of molecular cr
 ystals and oligopeptides - Mark E. Tuckerman\, New York University
DTSTART:20170519T100000Z
DTEND:20170519T110000Z
UID:TALK72672@talks.cam.ac.uk
CONTACT:Prof. Gabor Csanyi
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 exp
 eriment is fueling a powerful approach to address some of the most challen
 ging scientific problems. In this talk\, I will describe the efforts we ar
 e making in my group to develop new computational methodologies that addre
 ss specific challenges in free energy exploration and generation. In parti
 cular\, I will describe our recent development of enhanced free energy bas
 ed methodologies for predicting structure\, polymorphism\, and defects in 
 atomic and molecular crystals\, for exploring first-order phase transition
 s\, and for determining conformational equilibria of oligopeptides. The st
 rategies we are pursuing include large time-step molecular dynamics algori
 thms\, heterogeneous multiscale modeling and learning techniques\, which a
 llow “landmark” locations (minima and saddles) on a high-dimensional f
 ree energy surface to be mapped out\, and temperature-accelerated methods\
 , which allow relative free energies of the landmarks to be generated effi
 ciently and reliably. I will then discuss new schemes for using machine le
 arning techniques to represent and perform computations using multidimensi
 onal free energy surfaces. Finally\, if time permits\, I will describe the
  use of machine learning techniques to enhance the accuracy and efficiency
  of density functional theory calculations based on density learning model
 s.
LOCATION:Maxwell Centre\, JJ Thomson Avenue
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