Integrating Deep-Learning-Based Magnetic Model and Non-Collinear Spin-Constrained Method: Methodology, Implementation and Application Article Swipe
Daye Zheng
,
Xing‐Liang Peng
,
Yike Huang
,
Yinan Wang
,
Duo Zhang
,
Zhengtao Huang
,
Linfeng Zhang
,
Mohan Chen
,
Ben Xu
,
Weiqing Zhou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.14382
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.14382
We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic phenomena that requires large-scale simulations at the atomic level. First, we propose a basis-independent projection method for calculating atomic magnetic moments by applying a radial truncation to numerical atomic orbitals. A double-loop Lagrange multiplier method is utilized to ensure the satisfaction of constraint conditions while achieving accurate magnetic torque. The method is implemented in ABACUS with both plane wave basis and numerical atomic orbital basis. We benchmark the iron (Fe) systems and analyze differences from calculations with the plane wave basis and numerical atomic orbitals basis in describing magnetic energy barriers. Based on an automated workflow composed of first-principles calculations, magnetic model, active learning, and dynamics simulation, more than 30,000 first-principles data with the information of magnetic torque are generated to train a deep-learning-based magnetic model DeePSPIN for the Fe system. By utilizing the model in large-scale molecular dynamics simulations, we successfully predict Curie temperatures of alpha-Fe close to experimental values.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2501.14382
- https://arxiv.org/pdf/2501.14382
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406840956
All OpenAlex metadata
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https://openalex.org/W4406840956Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2501.14382Digital Object Identifier
- Title
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Integrating Deep-Learning-Based Magnetic Model and Non-Collinear Spin-Constrained Method: Methodology, Implementation and ApplicationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-24Full publication date if available
- Authors
-
Daye Zheng, Xing‐Liang Peng, Yike Huang, Yinan Wang, Duo Zhang, Zhengtao Huang, Linfeng Zhang, Mohan Chen, Ben Xu, Weiqing ZhouList of authors in order
- Landing page
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https://arxiv.org/abs/2501.14382Publisher landing page
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https://arxiv.org/pdf/2501.14382Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2501.14382Direct OA link when available
- Concepts
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Deep learning, Computer science, Spin (aerodynamics), Artificial intelligence, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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