تقرير
Quantifying the magnetic interactions governing chiral spin textures using deep neural networks
| العنوان: | Quantifying the magnetic interactions governing chiral spin textures using deep neural networks |
|---|---|
| المؤلفون: | Kong, Jian Feng, Ren, Yuhua, Tey, M. S. Nicholas, Ho, Pin, Khoo, Khoong Hong, Chen, Xiaoye, Soumyanarayanan, Anjan |
| سنة النشر: | 2023 |
| المجموعة: | Condensed Matter |
| مصطلحات موضوعية: | Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics |
| الوصف: | The interplay of magnetic interactions in chiral multilayer films gives rise to nanoscale topological spin textures, which form attractive elements for next-generation computing. Quantifying these interactions requires several specialized, time-consuming, and resource-intensive experimental techniques. Imaging of ambient domain configurations presents a promising avenue for high-throughput extraction of the parent magnetic interactions. Here we present a machine learning-based approach to determine the key interactions -- symmetric exchange, chiral exchange, and anisotropy -- governing chiral domain phenomenology in multilayers. Our convolutional neural network model, trained and validated on over 10,000 domain images, achieved $R^2 > 0.85$ in predicting the parameters and independently learned physical interdependencies between them. When applied to microscopy data acquired across samples, our model-predicted parameter trends are consistent with independent experimental measurements. These results establish ML-driven techniques as valuable, high-throughput complements to conventional determination of magnetic interactions, and serve to accelerate materials and device development for nanoscale electronics. Comment: 7 pages, 6 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2305.02954 |
| رقم الأكسشن: | edsarx.2305.02954 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |