Large language model-enabled machine learning for highperformance Nd-Fe-B permanent magnet design

Zhi Yang; Xiaoxiao Zhang; Weiqiang Liu; Jie Zhang; Mengya Tian; Yingying Ma; Haibo Xu; Xiao Chi; Yue Ming

doi:10.1039/d5ta10051h

January 1, 2026Journal of Materials Chemistry A

Large language model-enabled machine learning for highperformance Nd-Fe-B permanent magnet design

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Key Points

The aim is to enhance the design of high-performance Nd-Fe-B permanent magnets using machine learning techniques.
Utilized advanced machine learning algorithms to model material properties.
compared traditional approaches to the performance of machine learning models in the context of magnet design.
Assessed the efficiency and accuracy of the models in predicting magnet performance.
Identified key material properties that influence magnet performance more effectively than traditional methods.
Demonstrated significant time savings in materials discovery compared to conventional techniques.

Implication

Machine learning improves materials discovery for high-performance permanent magnets, suggesting efficiency gains.

Abstract

High-performance Nd-Fe-B permanent magnets are indispensable to modern energy-efficient technologies. Traditional materials discovery approaches are timeconsuming and resource-intensive, while existing machine learning models often fail to capture the rich contextual...

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Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/69b4fbf9b39f7826a300c91a https://doi.org/10.1039/d5ta10051h

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Large language model-enabled machine learning for highperformance Nd-Fe-B permanent magnet design

Key Points

Implication

Abstract

Discussion

Cite This Study

Large language model-enabled machine learning for highperformance Nd-Fe-B permanent magnet design

Key Points

Implication

Abstract

Discussion

Cite This Study

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