DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Sookil | ko |
dc.contributor.author | Park, Sohee | ko |
dc.contributor.author | Kim, Ki Woong | ko |
dc.contributor.author | Woo, Seong-Ihl | ko |
dc.contributor.author | Park, Sunwon | ko |
dc.date.accessioned | 2019-03-07T02:13:52Z | - |
dc.date.available | 2019-03-07T02:13:52Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2007-11 | - |
dc.identifier.citation | APPLIED SURFACE SCIENCE, v.254, no.3, pp.725 - 733 | - |
dc.identifier.issn | 0169-4332 | - |
dc.identifier.uri | http://hdl.handle.net/10203/250811 | - |
dc.description.abstract | During the last several years, the development of combinatorial technology has enabled synthesis of a huge amount of chemical compounds in a short time. The large number of variables makes the direct human interpretation of data derived from combinatorial experimentation for high-throughput screening (HTS) very difficult. Artificial neural networks using multilayer perceptrons (MLP) have been successfully applied to the regression problems with various material data. In this work, MLP model was applied to HTS of ferroelectric materials including Bi4-xLaxTi3O12 (BLT) and Bi4-xCexTi3O12 (BCT). The model using MLP was made to predict the ferroelectric properties of whole feasible experimental conditions. Once a neural network model with high accuracy and good generalization performance was established, we could predict the expected optimal reaction conditions with the best characteristics. The highest gradient value obtained using MLP model is higher than the maximum value found from experiments, thereby accelerating the discovery of the optimal compositions and post-annealing time of BCT and BLT. (C) 2007 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject | NETWORK-AIDED DESIGN | - |
dc.subject | NEURAL-NETWORK | - |
dc.subject | THIN-FILMS | - |
dc.subject | COMBINATORIAL CATALYSIS | - |
dc.subject | BISMUTH TITANATE | - |
dc.subject | TECHNOLOGY | - |
dc.title | High-throughput screening of ferroelectric materials for non-volatile random access memory using multilayer perceptrons | - |
dc.type | Article | - |
dc.identifier.wosid | 000253487400013 | - |
dc.identifier.scopusid | 2-s2.0-36148946053 | - |
dc.type.rims | ART | - |
dc.citation.volume | 254 | - |
dc.citation.issue | 3 | - |
dc.citation.beginningpage | 725 | - |
dc.citation.endingpage | 733 | - |
dc.citation.publicationname | APPLIED SURFACE SCIENCE | - |
dc.identifier.doi | 10.1016/j.apsusc.2007.05.097 | - |
dc.contributor.localauthor | Woo, Seong-Ihl | - |
dc.contributor.localauthor | Park, Sunwon | - |
dc.contributor.nonIdAuthor | Kang, Sookil | - |
dc.contributor.nonIdAuthor | Park, Sohee | - |
dc.contributor.nonIdAuthor | Kim, Ki Woong | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | artificial neural networks | - |
dc.subject.keywordAuthor | ferroelectric materials | - |
dc.subject.keywordAuthor | high-throughput screening | - |
dc.subject.keywordAuthor | multilayer perceptrons | - |
dc.subject.keywordAuthor | combinatorial chemistry | - |
dc.subject.keywordPlus | NETWORK-AIDED DESIGN | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | THIN-FILMS | - |
dc.subject.keywordPlus | COMBINATORIAL CATALYSIS | - |
dc.subject.keywordPlus | BISMUTH TITANATE | - |
dc.subject.keywordPlus | TECHNOLOGY | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.