| CONDENSED MATTER: ELECTRONIC STRUCTURE, ELECTRICAL, MAGNETIC, AND OPTICAL PROPERTIES |
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Machine Learning and Micromagnetic Studies of Magnetization Switching |
| Jing-Yue Miao1,2** |
1Key Laboratory of Advanced Materials (MOE), School of Materials Science and Engineering, Tsinghua University, Beijing 100084
2Argonne National Laboratory, Chicago, USA
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| Cite this article: |
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Jing-Yue Miao 2019 Chin. Phys. Lett. 36 097501 |
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Abstract Magnetization switching is one of the most fundamental topics in the field of magnetism. Machine learning (ML) models of random forest (RF), support vector machine (SVM), deep neural network (DNN) methods are built and trained to classify the magnetization reversal and non-reversal cases of single-domain particle, and the classification performances are evaluated by comparison with micromagnetic simulations. The results show that the ML models have achieved great accuracy and the DNN model reaches the best area under curve (AUC) of 0.997, even with a small training dataset, and RF and SVM models have lower AUCs of 0.964 and 0.836, respectively. This work validates the potential of ML applications in studies of magnetization switching and provides the benchmark for further ML studies in magnetization switching.
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Received: 24 May 2019
Published: 23 August 2019
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| PACS: |
75.78.Cd
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(Micromagnetic simulations ?)
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75.60.Jk
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(Magnetization reversal mechanisms)
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