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

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dc.creatorScott, D. J.
dc.creatorCoveney, P. V.
dc.creatorKilner, J. A.
dc.creatorRossiny, J. C. H.
dc.creatorAlford, N. Mc N.
dc.date2007-03-08
dc.date.accessioned2026-07-07T07:50:46Z
dc.date.available2026-07-07T07:50:46Z
dc.descriptionWe 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.
dc.identifierhttps://arxiv.org/abs/cond-mat/0703210
dc.identifierhttp://arxiv.org/abs/cond-mat/0703210
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/125279
dc.subjectMaterials Science
dc.subjectDisordered Systems and Neural Networks
dc.titlePrediction of the functional properties of ceramic materials from composition using artificial neural networks
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