University of Cambridge > Talks.cam > TCM Journal Club > Potential Energy Surfaces Fitted by Artificial Neural Networks

Potential Energy Surfaces Fitted by Artificial Neural Networks

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Daniel Cole.

Chris M. Handley and Paul L. A. Popelier, J. Phys. Chem. A 2010 , 114, 3371-3383

Molecular mechanics is the tool of choice for the modeling of systems that are 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 potentials that are able to quickly reproduce ab initio quality results at the fraction of the cost. The interactions within force fields are represented by 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 examples of a new design ethic, where novel and contemporary methods using machine learning, in particular, artificial neural networks, have been used to find the nature of the underlying functions of a force field. Here we appraise what has been achieved over this time and what requires further improvements, while offering some insight and guidance for the development of future force fields.

This talk is part of the TCM Journal Club series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity