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• W2070654038 abstract "The objective of this paper is to show how one may design combinatorial libraries a priori by integrating data mining techniques with physically robust multivariate data. It is shown that large datasets can be developed from relatively small amounts of experimental and theoretically based information. This involves a process of strategically selecting appropriate physical based parameters that can be analyzed in a multivariate manner. In this paper we identify for the first time the bandgap and lattice parameters of nearly 200 stoichiometries of new and yet to be synthesized compound chalcopyrite semiconductors. The robustness of this “virtual” combinatorial experimentation approach is demonstrated by comparison to band gap predictions from theoretical studies on a range of compositions for a selected quaternary compound semiconductor." @default.
• W2070654038 created "2016-06-24" @default.
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• W2070654038 date "2004-02-01" @default.
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• W2070654038 title "Combinatorial design of semiconductor chemistry for bandgap engineering: “virtual” combinatorial experimentation" @default.
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• W2070654038 doi "https://doi.org/10.1016/s0169-4332(03)00918-8" @default.
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