Prediction of the functional properties of ceramic materials from composition using artificial neural networks

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Title: Prediction of the functional properties of ceramic materials from composition using artificial neural networks
Authors: Scott, DJ
Coveney, PV
Kilner, JA
Rossiny, JCH
Alford, NMN
Item Type: Journal Article
Abstract: We describe the development of artificial neural networks (ANN) for the prediction of the properties of ceramic materials. The ceramics studied here include polycrystalline, inorganic, non-metallic materials and are investigated on the basis of their dielectric and ionic properties. Dielectric materials are of interest in telecommunication applications, where they are used in tuning and filtering equipment. Ionic and mixed conductors are the subjects of a concerted effort in the search for new materials that can be incorporated into efficient, clean electrochemical devices of interest in energy production and greenhouse gas reduction applications. Multi-layer perceptron ANNs are trained using the back-propagation algorithm and utilise data obtained from the literature to learn composition–property relationships between the inputs and outputs of the system. The trained networks use compositional information to predict the relative permittivity and oxygen diffusion properties of ceramic materials. The results show that ANNs are able to produce accurate predictions of the properties of these ceramic materials, which can be used to develop materials suitable for use in telecommunication and energy production applications.
Issue Date: 20-Apr-2007
Date of Acceptance: 23-Feb-2007
URI: http://hdl.handle.net/10044/1/60857
DOI: https://dx.doi.org/10.1016/j.jeurceramsoc.2007.02.212
ISSN: 0955-2219
Publisher: Elsevier
Start Page: 4425
End Page: 4435
Journal / Book Title: Journal of the European Ceramic Society
Volume: 27
Issue: 16
Copyright Statement: © 2007 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: GR/S85238/01
Keywords: Science & Technology
Technology
Materials Science, Ceramics
Materials Science
dielectric properties
ionic conductivity
perovskites
functional applications
neural networks
ELECTRICAL-PROPERTIES
MICROWAVE APPLICATIONS
DIELECTRIC-CONSTANTS
PERFORMANCE
FORMULATION
SELECTION
CEMENTS
DESIGN
MODEL
QSPR
cond-mat.mtrl-sci
cond-mat.dis-nn
0912 Materials Engineering
Materials
Publication Status: Published
Online Publication Date: 2007-04-20
Appears in Collections:Faculty of Engineering
Materials
Faculty of Natural Sciences



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