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

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

Combinatorial Bulk Ceramic Magnetoelectric Composite Libraries of Strontium Hexaferrite and Barium Titanate

Bulk ceramic combinatorial libraries were produced via a novel, high-throughput (HT) process, in the form of polycrystalline strips with a gradient composition along the length of the library. Step gradient ceramic composite libraries with 10 mol % steps of SrFe 12O 19-BaTiO 3 (SrM-BT) were made and characterized using HT methods, as a proof of principle of the combinatorial bulk ceramic process, and sintered via HT thermal processing. It was found that the SrM-BT libraries sintered at 1175 °C had the optimum morphology and density. The compositional, electrical and magnetic properties of this library were analyzed, and it was found that the SrM and BT phases did not react and remained discrete. The combinatorial synthesis method produced a relatively linear variation in composition. The magnetization of the library followed the measured compositions very well, as did the low frequency permittivity values of most compositions in the library. However, with high SrM content of ≥80 mol %, the samples became increasingly conductive, and no reliable dielectric measurements could be made. Such conductivity would also greatly inhibit any ferroelectricity and magnetoelectric coupling with these composites with high levels of the SrM hexagonal ferrite. © 2012 American Chemical Society

Functional ceramic materials database: an online resource for materials research

We present work on the creation of a ceramic materials database which contains data gleaned from literature data sets as well as new data obtained from combinatorial experiments on the London University Search Instrument. At the time of this writing, the database contains data related to two main groups of materials, mainly in the perovskite family. Permittivity measurements of electroceramic materials are the first area of interest, while ion diffusion measurements of oxygen ion conductors are the second. The nature of the database design does not restrict the type of measurements which can be stored; as the available data increase, the database may become a generic, publicly available ceramic materials resource