Extracting key magnetic properties of materials using machine learning

This work demonstrates a machine-learning approach to quantitatively reconstruct the spin-spin interactions in complex magnetic systems using magnetometry data only. This offers a new solution to construct spin models in a high-throughput manner, significantly enhancing the efficiency of the exploration of new topological spintronic materials.

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Published
2025-09-18
DOI
https://doi.org/10.1117/12.3064115
Primary Topic
Non-Destructive Testing Techniques
Type
article
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article

Extracting key magnetic properties of materials using machine learning

Gen Yin
Non-Destructive Testing Techniques
article

Extracting key magnetic properties of materials using machine learning

Gen Yin
article en

Abstract

This work demonstrates a machine-learning approach to quantitatively reconstruct the spin-spin interactions in complex magnetic systems using magnetometry data only. This offers a new solution to construct spin models in a high-throughput manner, significantly enhancing the efficiency of the exploration of new topological spintronic materials.

Georgetown University (US)
Openalex Percentile: Top 74%
Non-Destructive Testing Techniques
DOISourceOpenAlex
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Extracting key magnetic properties of materials using machine learning — Gen Yin · (2025) | TGRS Research Map | TGRS