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

Data-driven technique is a powerful and efficient tool for guiding materials design, which could supply as an alternative 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 (H-cj) and maximum magnetic energy product ((BH)(max)) via machine learning has been applied to (PrNd-La-Ce)(2)Fe14B 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 R-2 = 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 accurately 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.