Origins of hole traps in hydrogenated nanocrystalline and amorphous silicon revealed through machine learning

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@Article{Mueller:2014:PhysRevB,
  author =       "Tim Mueller and Eric Johlin and Jeffrey C. Grossman",
  title =        "Origins of hole traps in hydrogenated nanocrystalline
                 and amorphous silicon revealed through machine
                 learning",
  journal =      "Physical Review B",
  year =         "2014",
  month =        mar,
  number =       "11",
  pages =        "115202",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1098-0121; 1550-235X",
  bibsource =    "OAI-PMH server at dspace.mit.edu",
  description =  "Center for Clean Water and Clean Energy at MIT and
                 KFUPM (Project R1-CE-08); National Science Foundation
                 (U.S.) (Grant 1035400)",
  language =     "en_US",
  oai =          "oai:dspace.mit.edu:1721.1/88769",
  URL =          "http://hdl.handle.net/1721.1/88769",
  DOI =          "doi:10.1103/PhysRevB.89.115202",
  size =         "7 pages",
  abstract =     "Genetic programming is used to identify the structural
                 features most strongly associated with hole traps in
                 hydrogenated nanocrystalline silicon with very low
                 crystalline volume fraction. The genetic programming
                 algorithm reveals that hole traps are most strongly
                 associated with local structures within the amorphous
                 region in which a single hydrogen atom is bound to two
                 silicon atoms (bridge bonds), near fivefold coordinated
                 silicon (floating bonds), or where there is a
                 particularly dense cluster of many silicon atoms. Based
                 on these results, we propose a mechanism by which deep
                 hole traps associated with bridge bonds may contribute
                 to the Staebler-Wronski effect.",
}

Genetic Programming entries for Tim Mueller Eric Johlin Jeffrey C Grossman

Citations