# 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:1.3973

@Misc{oai:arXiv.org:cs/0608078,
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 arXiv.org",
oai =          "oai:arXiv.org:cs/0608078",
howpublished = "arXiv",
keywords =     "genetic algorithms, genetic programming, Computer
Science, Neural and Evolutionary Computing, Artificial
Intelligence",
URL =          "http://arxiv.org/abs/cs/0608078",
URL =          "http://arxiv.org/PS_cache/cs/pdf/0608/0608078v1.pdf",
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
architecture.",
notes =        "Published as \cite{journals/jcc/SlepoyPT07} ?",
}



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

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