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

TL;DR

This paper develops artificial neural networks to predict dielectric and ionic properties of ceramic materials from their composition, aiding in the design of materials for telecommunications and energy applications.

Contribution

It introduces a neural network approach trained on literature data to accurately predict ceramic properties based on composition, advancing materials design methods.

Findings

ANNs accurately predict dielectric properties

ANNs effectively estimate oxygen diffusion properties

Potential for designing ceramics for specific applications

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.