Searching for globally optimal functional forms for interatomic potentials using genetic programming with parallel tempering

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@Article{journals/jcc/SlepoyPT07,
  author =       "A. Slepoy and M. D. Peters and A. P. Thompson",
  title =        "Searching for globally optimal functional forms for
                 interatomic potentials using genetic programming with
                 parallel tempering",
  journal =      "Journal of Computational Chemistry",
  year =         "2007",
  volume =       "28",
  number =       "15",
  pages =        "2465--2471",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1002/jcc.20710",
  abstract =     "Molecular dynamics and other molecular simulation
                 methods rely on a potential energy function, based only
                 on the relative coordinates of the atomic nuclei. Such
                 a function, called a force field, approximately
                 represents the electronic structure interactions of a
                 condensed matter system. Developing such approximate
                 functions and fitting their parameters remains an
                 arduous, time-consuming process, relying on expert
                 physical intuition. To address this problem, a
                 functional programming methodology was developed that
                 may enable automated discovery of entirely new
                 force-field functional forms, while simultaneously
                 fitting parameter values. The method uses a combination
                 of genetic programming, Metropolis Monte Carlo
                 importance sampling and parallel tempering, to
                 efficiently search a large space of candidate
                 functional forms and parameters.

                 The methodology was tested using a nontrivial problem
                 with a well-defined globally optimal solution: a small
                 set of atomic configurations was generated and the
                 energy of each configuration was calculated using the
                 Lennard-Jones pair potential. Starting with a
                 population of random functions, our fully automated,
                 massively parallel implementation of the method
                 reproducibly discovered the original Lennard-Jones pair
                 potential by searching for several hours on 100
                 processors, sampling only a minuscule portion of the
                 total search space. This result indicates that, with
                 further improvement, the method may be suitable for
                 unsupervised development of more accurate force fields
                 with completely new functional forms.",
  notes =        "See also \cite{oai:arXiv.org:cs/0608078}. Multiscale
                 Dynamic Materials Modeling Department, Sandia National
                 Laboratories, Albuquerque, New Mexico 87185",
  bibdate =      "2007-11-27",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/jcc/jcc28.html#SlepoyPT07",
}

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

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