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
