2020/08/20 by Duong-Nguyen Nguyen, Duong‐Nguyen Nguyen, Tien-Lam Pham +11
Computer Science · Materials Science · Mathematics · Physics and Astronomy · #Electron and X-Ray Spectroscopy Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #X-ray Diffraction in Crystallography #cond-mat.mtrl-sci #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2008.08818
10 pages, 3 figures
arxiv created 2020/08/20 · openalex publication_date 2020/08/20 · arxiv updated 2020/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.