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SUMMARY:Gaussian process regression in molecular modelling - Csanyi\, G (U
 niversity of Cambridge)
DTSTART:20140317T094500Z
DTEND:20140317T103500Z
UID:TALK51453@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:The talk will summarise our recent work in using Gaussian proc
 ess regression (a form of Bayesian non-parametric inference) to model mode
 rate to high dimensional functions in chemistry. Applications range from c
 onstructing interatomic potentials (i.e. parametrisations of the Born-Oppe
 nheimer potential energy surface) starting from total energy electronic st
 ructure calculations\, to free energy surface reconstruction based on umbr
 ella sampling trajectories. In every case studied so far\, a careful treat
 ment of data representation and hyperparameters leads to huge increases in
  computational efficiency and model fidelity.\n
LOCATION:Seminar Room 1\, Newton Institute
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