Please use this identifier to cite or link to this item:
|Title:||On the prediction of ternary semiconductor properties by artificial intelligence methods||Authors:||Zeng, Y.
|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. https://doi.org/10.1021/cm0103996||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||URI:||http://scholarbank.nus.edu.sg/handle/10635/82818||ISSN:||08974756||DOI:||10.1021/cm0103996|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.