In:
Journal of Materials Chemistry A, Royal Society of Chemistry (RSC), Vol. 11, No. 16 ( 2023), p. 8988-9001
Abstract:
Designing Nd–Fe–B-based permanent magnets with exceptional high temperature stability is a critical step for extending their use in traction motors with an operating temperature of ∼150 °C. Conventionally, the high temperature stability is achieved through doping heavy rare-earth elements such as Dy, Tb, etc. , which leads to elevated cost in the meantime. Efforts towards doping Nd–Fe–B with lower-cost elements such as La, Ce, and Y, and leveraging the temperature stability with Co and Ni to retain high-temperature performance (remanence and coercivity) have been underway for many years. A critical challenge, however, is the cost in time and resource required for optimizing the doping concentration of these species on a trial-and-error basis. In this work, we demonstrate the utilization of the ‘adaptive’ learning framework (based on Bayesian Optimization) in the composition optimization of Nd–Fe–B-based magnets: (Nd 80 Pr 20 ) 30.80− x − y − z La x Ce y Y z Fe 66.67− u − w Co u Ni w B 0.96 M 1.57 (M = Al, Cu, Ga, Ti, in wt%) towards an improved performance–cost ratio. Starting from a limited set of 24 compositions, 9 novel compositions were recommended within 3 iterations, which were then experimentally fabricated, with their magnetic properties measured. The best two candidates identified in the last iteration showed 18.4% and 13.1% improvement in the performance–cost ratio with respect to the benchmark Nd–Fe–B, respectively. The adaptive learning framework proved efficient in screening novel compositions and guiding the experimental design of Nd–Fe–B-based permanent magnets in this work, suggesting great promise for its adoption for other multi-component systems targeting an improved performance–cost ratio.
Type of Medium:
Online Resource
ISSN:
2050-7488
,
2050-7496
Language:
English
Publisher:
Royal Society of Chemistry (RSC)
Publication Date:
2023
detail.hit.zdb_id:
2702232-8
