Abstract:The prediction of glass-forming ability (GFA) in alloy systems is a challenging problem in material science as well as for metallurgical applications. In this study, we build artificial neural network (ANN) models to investigate the GFA of multicomponent alloys, based on the datasets assembled from ternary alloys as well as quinary alloys prepared by magnetron sputtering. Through training the ANN models with different combinations of datasets, we tackle the problem of the influence of the data source on the mo… Show more
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