Significance of artificial neural network analytical models in materials' performance prediction

In materials science, performance prediction of materials plays an important role in improving the quality of materials as well as preventing serious damage to the environment and threat to public safety. Traditional regression analysis models in materials science are not yet perfect, limited to capture nonlinearities of data and time-consumption for prediction, and have a poor ability to handle a large amount of data. This leads to a demand for analyses of materials data using novel computer science methods. In recent years, artificial neural networks (ANNs) are increasingly performing as a strong tool to establish the relationships among data and being successfully applied in materials science due to their generalization ability, noise tolerance and fault tolerance. In this paper, some typical ANN applications for predicting various properties (corrosion, structural, tribological and so on) of different materials serving multiple environments (atmosphere, stress, weld and so on) are reviewed. It highlights the significance of ANN in materials-related problems in separate sections arranged by the level of simplicity, ranging from simple ANN models alone to more complicated ANN models along with the hybrid use of other computing and input-ranking methods, and the trend of ANN in the context of materials science with some limitations.