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)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 H_cj and (BH)_max, with high accuracies of R²=0.88 and 0.89, respectively. Using the best models, predicted datasets of H_cj 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.