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