Ensemble learning reveals dissimilarity between rare-earth transition metal binary alloys with respect to the Curie temperature
This provides a potential tool for materials scientists to better understand data structure with respect to specific properties, though it appears incremental as an adaptation of existing ensemble and regression techniques.
The authors tackled the problem of measuring dissimilarity between materials with respect to a target physical property by proposing a data-driven ensemble method using kernel ridge regression and Gaussian mixture models. They applied this method to Curie temperature data of rare-earth transition metal binary alloys and demonstrated it effectively reveals meaningful material relationships.
We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting model; multiple random subset sampling of the materials is done to generate prediction models and the corresponding contributions of the reference training materials in detail. The distribution of the predicted values for each material can be approximated by a Gaussian mixture model. The reference training materials contributed to the prediction model that accurately predicts the physical property value of a specific material, are considered to be similar to that material, or vice versa. Evaluations using synthesized data demonstrate that the proposed method can effectively measure the dissimilarity between data instances. An application of the analysis method on the data of Curie temperature (TC) of binary 3d transition metal 4f rare earth binary alloys also reveals meaningful results on the relations between the materials. The proposed method can be considered as a potential tool for obtaining a deeper understanding of the structure of data, with respect to a target property, in particular.