Composition design for (PrNd–La Ce)2Fe14B melt-spun magnets by machine learning technique

• 来源期刊: 中国物理B（英文版）
• 关键词: permanent magnet materials design machine learning property prediction
• 年，卷（期）: 2018,（4）
• 页码范围: 460-464
• 页数: 5页
• 语种: 英文
• DOI: 10.1088/1674-1056/27/4/047501

摘要

Data-driven technique is a powerful and efficient tool for guiding materials design, which could supply as an alterna-tive to trial-and-error experiments. In order to accelerate composition design for low-cost rare-earth permanent magnets, an approach using composition to estimate coercivity (Hcj) and maximum magnetic energy product ((BH)max) via machine learning has been applied to (PrNd–La–Ce)2Fe14B melt-spun magnets. A set of machine learning algorithms are employed to build property prediction models, in which the algorithm of Gradient Boosted Regression Trees is the best for predicting both Hcj and (BH)max, with high accuracies of R2=0.88 and 0.89, respectively. Using the best models, predicted datasets of Hcj or (BH )max in high-dimensional composition space can be constructed. Exploring these virtual datasets could provide efficient guidance for materials design, and facilitate the composition optimization of 2:14:1 structure melt-spun magnets. Combined with magnets' cost performance, the candidate cost-effective magnets with targeted properties can also be accu-rately and rapidly identified. Such data analytics, which involves property prediction and composition design, is of great time-saving and economical significance for the development and application of LaCe-containing melt-spun magnets.