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:1.7615

@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

Citations