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Title: On the prediction of ternary semiconductor properties by artificial intelligence methods
Authors: Zeng, Y.
Chua, S.J. 
Wu, P.
Issue Date: 2002
Citation: Zeng, Y., Chua, S.J., Wu, P. (2002). On the prediction of ternary semiconductor properties by artificial intelligence methods. Chemistry of Materials 14 (7) : 2989-2998. ScholarBank@NUS Repository.
Abstract: Band gap energy and lattice constant of ternary semiconductors (ABC2 chalcopyrites) are correlated to their chemical stoichiometrics and fundamental element properties of the constituents, through artificial intelligence techniques and sublattice model without prior knowledge of the electronic structures of individual compounds. New chalcopyrite semiconductors of I-III-VI2 and II-IV-V2 types are then predicted by using the developed model. Many potential semiconductors are selected from 270 possible new chalcopyrites by screening the element periodic table. The predicted band gap energy and lattice constant of the potential semiconductors fall in the range of 0.10-4.96 eV and 2.22-10.14 Å, respectively, which may offer a useful guideline in the exploration of new semiconductors for a wide range of applications by further experimental or first principles studies.
Source Title: Chemistry of Materials
ISSN: 08974756
DOI: 10.1021/cm0103996
Appears in Collections:Staff Publications

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