Please use this identifier to cite or link to this item: https://doi.org/10.1021/cm0103996
DC FieldValue
dc.titleOn the prediction of ternary semiconductor properties by artificial intelligence methods
dc.contributor.authorZeng, Y.
dc.contributor.authorChua, S.J.
dc.contributor.authorWu, P.
dc.date.accessioned2014-10-07T04:33:52Z
dc.date.available2014-10-07T04:33:52Z
dc.date.issued2002
dc.identifier.citationZeng, 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
dc.identifier.issn08974756
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/82818
dc.description.abstractBand 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1021/cm0103996
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1021/cm0103996
dc.description.sourcetitleChemistry of Materials
dc.description.volume14
dc.description.issue7
dc.description.page2989-2998
dc.description.codenCMATE
dc.identifier.isiut000176929000029
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

23
checked on Jul 28, 2021

WEB OF SCIENCETM
Citations

18
checked on Jul 20, 2021

Page view(s)

88
checked on Jul 15, 2021

Google ScholarTM

Check

Altmetric


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