Chapter 14 - Evolutionary Algorithms Applied to Electronic-Structure Informatics: Accelerated Materials Design Using Data Discovery vs. Data Searching

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Johnson:2013:IMSE,
  author =       "Duane D. Johnson",
  title =        "Chapter 14 - Evolutionary Algorithms Applied to
                 Electronic-Structure Informatics: Accelerated Materials
                 Design Using Data Discovery vs. Data Searching",
  editor =       "Krishna Rajan",
  booktitle =    "Informatics for Materials Science and Engineering",
  publisher =    "Butterworth-Heinemann",
  address =      "Oxford",
  year =         "2013",
  pages =        "349--364",
  isbn13 =       "978-0-12-394399-6",
  DOI =          "doi:10.1016/B978-0-12-394399-6.00014-X",
  URL =          "http://www.sciencedirect.com/science/article/pii/B978012394399600014X",
  abstract =     "We exemplify and propose extending the use of genetic
                 programs (GPs) - a genetic algorithm (GA) that evolves
                 computer programs via mechanisms similar to genetics
                 and natural selection - to symbolically regress key
                 functional relationships between materials data,
                 especially from electronic structure. GPs can extract
                 structure-property relations or enable simulations
                 across multiple scales of time and/or length. Uniquely,
                 GP-based regression permits {"}data discovery{"} -
                 finding relevant data and/or extracting correlations
                 (data reduction/data mining) - in contrast to searching
                 for what you know, or you think you know (intuition).
                 First, catalysis-related materials correlations are
                 discussed, where simple electronic-structure-based
                 rules are revealed using well-developed intuition, and
                 then, after introducing the concepts, GP regression is
                 used to obtain (i) a constitutive relation between flow
                 stress and strain rate in aluminium, and (ii)
                 multi-time-scale kinetics for surface alloys. We close
                 with some outlook for a range of applications
                 (materials discovery, excited-state chemistry, and
                 multiscaling) that could rely primarily on density
                 functional theory results.",
  keywords =     "genetic algorithms, genetic programming, Electronic
                 structure, Density functional theory, Evolutionary
                 algorithms, Genetic programs, Informatics",
}

Genetic Programming entries for Duane D Johnson

Citations