Smooth Overlap of Spin Orientations: Machine Learning Exchange Fields for Ab-initio Spin Dynamics

[2403.10769] Smooth Overlap of Spin Orientations: Machine Learning Exchange Fields for Ab-initio Spin Dynamics Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Condensed Matter > Materials Science arXiv:2403.10769 (cond-mat) [Submitted on 16 Mar 2024 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: Smooth Overlap of Spin Orientations: Machine Learning Exchange Fields for Ab-initio Spin Dynamics Authors: Yuqiang Gao , Menno Bokdam , Paul J. Kelly View a PDF of the paper titled Smooth Overlap of Spin Orientations: Machine Learning Exchange Fields for Ab-initio Spin Dynamics, by Yuqiang Gao and 2 other authors View PDF HTML (experimental) Abstract: We add the magnetic degrees of freedom to the widely used Gaussian Approximation Potential of machine learning (ML) and present a model that describes the potential energy surface of a crystal based on the atomic coordinates as well as their noncollinear magnetic moments. Assuming an adiabatic approximation for the spin directions and magnitudes, the ML model depends solely on spin coordinates and orientation, resulting in computational efffciency and enabling ab initio spin dynamics. Leveraging rotational symmetries of magnetic interactions, the ML model can incorporate various magnetic interactions, expanding into two-body, three-body terms, etc., following the spirit of cluster expansion. For simplicity, we implement the ML model with a two-body form for the exchange interaction. Comparing total energies and local fields predicted by the model for noncollinear spin arrangements with explicit results of constrained noncollinear density functional calculations for bcc Fe yields excellent results, within 1 meV/spin for the total energy. Further optimization, including three-body and other terms, is expected to encompass diverse magnetic interactions and enhance the model's accuracy. This will extend the model's applicability to a wide range of materials and facilitate the machine learning ab initio spin dynamics. Comments: 16 pages, 7 figures Subjects: Materials Science (cond-mat.mtrl-sci) ; Mesoscale and Nanoscale Physics (cond-mat.mes-hall) Cite as: arXiv:2403.10769 [cond-mat.mtrl-sci] (or arXiv:2403.10769v3 [cond-mat.mtrl-sci] for this version) https://doi.org/10.48550/arXiv.2403.10769 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Phys. Rev. B 113, 144413 (2026) Related DOI : https://doi.org/10.1103/kknv-7ypx Focus to learn more DOI(s) linking to related resources Submission history From: Yuqiang Gao [ view email ] [v1] Sat, 16 Mar 2024 02:15:40 UTC (4,886 KB) [v2] Sun, 20 Jul 2025 08:26:02 UTC (4,661 KB) [v3] Fri, 10 Apr 2026 03:24:33 UTC (5,113 KB) Full-text links: Access Paper: View a PDF of the paper titled Smooth Overlap of Spin Orientations: Machine Learning Exchange Fields for Ab-initio Spin Dynamics, by Yuqiang Gao and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cond-mat.mtrl-sci < prev | next > new | recent | 2024-03 Change to browse by: cond-mat cond-mat.mes-hall References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Fully extended r-spin TQFTs#344751
• Spin Crossover under Extreme Conditions: A Structural Approach#384330
• Spin Inertia as a Source of Topological Magnons: Chiral Edge States from Coupled Precession and Nutation#1012147
• Ab Initio Spinor Kadanoff-Baym Approach to Nonequilibrium Electron, Phonon and Magnon Dynamics in Itinerant Ferromagnets#1105069
• Ultralight Bosons Explain the Mass-Spin Correlations in the Merging Binary Black Hole Population#924821