稀土过渡金属二元合金居里温度的差异
Ensemble learning reveals dissimilarity between rare-earth transition metal binary alloys with respect to the Curie temperature
摘要：
我们提出了一种数据驱动的方法来提取材料之间的相异性，相对于一个给定的目标物理特性。该方法基于集成方法，以核岭回归为预测模型，对样本进行多个随机子集采样，生成预测模型，并详细分析参考训练样本的贡献。每种材料的预测值的分布可以通过高斯混合模型来近似。有助于准确预测特定材料的物理性质值的预测模型的参考训练材料被认为与该材料相似，反之亦然。使用合成数据的评估表明，该方法可以有效地测量数据实例之间的差异。将该方法应用于3d过渡金属4f稀土二元合金的居里温度（TC）数据，也揭示了材料间关系的有意义的结果。所提出的方法可以被认为是一种潜在的工具，用于获得更深入的理解的数据的结构，相对于一个目标属性，特别是。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
