High-throughput screening of ferroelectric materials for non-volatile random access memory using multilayer perceptrons

Cited 7 time in webofscience Cited 6 time in scopus
  • Hit : 155
  • Download : 0
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.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2007-11
Language
English
Article Type
Article
Keywords

NETWORK-AIDED DESIGN; NEURAL-NETWORK; THIN-FILMS; COMBINATORIAL CATALYSIS; BISMUTH TITANATE; TECHNOLOGY

Citation

APPLIED SURFACE SCIENCE, v.254, no.3, pp.725 - 733

ISSN
0169-4332
DOI
10.1016/j.apsusc.2007.05.097
URI
http://hdl.handle.net/10203/250811
Appears in Collection
CBE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 7 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0