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SUMMARY:Potential Energy Surfaces Fitted by Artificial Neural Networks - W
 ojciech Szlachta (TCM)
DTSTART:20100528T150000Z
DTEND:20100528T153000Z
UID:TALK24569@talks.cam.ac.uk
CONTACT:Daniel Cole
DESCRIPTION:"Chris M. Handley and Paul L. A. Popelier\, J. Phys. Chem. A 2
 010\, 114\, 3371-3383":http://pubs.acs.org/doi/abs/10.1021/jp9105585\n\nMo
 lecular mechanics is the tool of choice for the modeling of systems that a
 re so large or complex that it is impractical or impossible to model them 
 by ab initio methods. For this reason there is a need for accurate potenti
 als that are able to quickly reproduce ab initio quality results at the fr
 action of the cost. The interactions within force fields are represented b
 y a number of functions. Some interactions are well understood and can be 
 represented by simple mathematical functions while others are not so well 
 understood and their functional form is represented in a simplistic manner
  or not even known. In the last 20 years there have been the first example
 s of a new design ethic\, where novel and contemporary methods using machi
 ne learning\, in particular\, artificial neural networks\, have been used 
 to find the nature of the underlying functions of a force field. Here we a
 ppraise what has been achieved over this time and what requires further im
 provements\, while offering some insight and guidance for the development 
 of future force fields.
LOCATION:TCM Seminar Room\, Cavendish Laboratory
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