BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Intra- and intermolecular potential energy surfaces derived from a
 b initio data by machine learning - Alan Nichol (Eng) 
DTSTART:20131113T114000Z
DTEND:20131113T120000Z
UID:TALK48445@talks.cam.ac.uk
CONTACT:Dr. Mike Towler
DESCRIPTION:In recent years it has become possible to predict the properti
 es of molecular materials using potential energy surfaces fitted only to a
 b initio calculations. With current methods and hardware it feasible to so
 lve the\nSchroedinger equation at the CCSD(T) level of accuracy for at mos
 t a few molecules at a time. Unfortunately this and other quantum chemistr
 y methods scale steeply with system size [O(N7) for CCSD(T)] so in many ca
 ses cannot be applied. Much effort has been devoted to developing potentia
 l energy surfaces which map the accurate quantum mechanical results to fun
 ctional\nforms which can be evaluated at vastly reduced expense. A number 
 of methods exist for fitting these potentials\, most of which require a gr
 eat deal of expert knowledge\, iterative improvement\, or both. We use the
  recently\ndeveloped method of Gaussian Approximation Potentials (GAP) to 
 improve upon this process in several ways. GAP makes use of Gaussian Proce
 ss regression\, a principled\, automatic\, nonparametric\, Bayesian approa
 ch to function\nfitting. We show how intra- and intermolecular potential e
 nergy surfaces for arbitrary molecules can be made automatically using GAP
 \, and how these can be made systematically more accurate by including mor
 e training data.
LOCATION:TCM Seminar Room\, Cavendish Laboratory
END:VEVENT
END:VCALENDAR