Please use this identifier to cite or link to this item: https://doi.org/10.1021/cm0103996
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. 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.

SCOPUSTM   
Citations

17
checked on May 15, 2018

WEB OF SCIENCETM
Citations

14
checked on May 15, 2018

Page view(s)

26
checked on Apr 20, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.