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SUMMARY:Nested sampling with Hamiltonian Monte Carlo - Robert Baldock (Uni
 versity of Cambridge)
DTSTART:20160203T110000Z
DTEND:20160203T113000Z
UID:TALK64297@talks.cam.ac.uk
CONTACT:Joseph Nelson
DESCRIPTION:Nested sampling is a Monte Carlo algorithm that can be used to
  efficiently calculate the complete configurational density of states in a
  material that undergoes multiple first-order phase transitions. From the 
 density of states one can calculate the partition function as an explicit 
 function of temperature and perform statistical mechanics from first princ
 iples. Indeed\, we have shown how nested sampling can be used to automatic
 ally discover complete pressure-temperature phase diagrams with no prior k
 nowledge of the locations of phase transitions or the structures of phases
 . In this talk I will present a new version of the nested sampling algorit
 hm\, based on a modified Hamiltonian ("Hybrid") Monte Carlo scheme. This n
 ew scheme reduces the scaling of a general nested sampling calculation. In
  particular the new algorithm expedites the sampling of atomic configurati
 on spaces in condensed phases\, and permits one to perform nested sampling
  calculations at a fraction of the cost required by ordinary nested sampli
 ng with standard Monte Carlo.
LOCATION:TCM Seminar Room\, Cavendish Laboratory
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