Searching for Globally Optimal Functional Forms for Inter-Atomic Potentials Using Parallel Tempering and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  title =        "Searching for Globally Optimal Functional Forms for
                 Inter-Atomic Potentials Using Parallel Tempering and
                 Genetic Programming",
  author =       "A. Slepoy and A. P. Thompson and M. D. Peters",
  year =         "2006",
  month =        aug # "~18",
  bibsource =    "OAI-PMH server at",
  oai =          "",
  howpublished = "arXiv",
  keywords =     "genetic algorithms, genetic programming, Computer
                 Science, Neural and Evolutionary Computing, Artificial
  URL =          "",
  URL =          "",
  size =         "11 pages",
  abstract =     "We develop a Genetic Programming-based methodology
                 that enables discovery of novel functional forms for
                 classical inter-atomic force-fields, used in molecular
                 dynamics simulations. Unlike previous efforts in the
                 field, that fit only the parameters to the fixed
                 functional forms, we instead use a novel algorithm to
                 search the space of many possible functional forms.
                 While a follow-on practical procedure will use
                 experimental and {\it ab inito} data to find an optimal
                 functional form for a forcefield, we first validate the
                 approach using a manufactured solution. This validation
                 has the advantage of a well-defined metric of success.
                 We manufactured a training set of atomic coordinate
                 data with an associated set of global energies using
                 the well-known Lennard-Jones inter-atomic potential. We
                 performed an automatic functional form fitting
                 procedure starting with a population of random
                 functions, using a genetic programming functional
                 formulation, and a parallel tempering Metropolis-based
                 optimisation algorithm. Our massively-parallel method
                 independently discovered the Lennard-Jones function
                 after searching for several hours on 100 processors and
                 covering a miniscule portion of the configuration
                 space. We find that the method is suitable for
                 unsupervised discovery of functional forms for
                 inter-atomic potentials/force-fields. We also find that
                 our parallel tempering Metropolis-based approach
                 significantly improves the optimization convergence
                 time, and takes good advantage of the parallel cluster
  notes =        "Published as \cite{journals/jcc/SlepoyPT07} ?",

Genetic Programming entries for Alex Slepoy Aidan P Thompson Michael D Peters