mlsanityData to 7 Oct 2026

Spin-Dependent Graph Neural Network Potential for Magnetic Materials

arXiv 2203.02853 ↗First posted 6 Mar 2022physics.comp-ph cond-mat.dis-nn cs.LGcited by 9

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Abstract (arXiv)

The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals. However, creating interatomic potentials for magnetic systems that account for both magnetic moments and structural degrees of freedom remains a challenge. This work introduces SpinGNN, a spin-dependent interatomic potential approach that employs the graph neural network (GNN) to describe magnetic systems. SpinGNN consists of two types of edge GNNs: Heisenberg edge GNN (HEGNN) and spin-distance edge GNN (SEGNN). HEGNN is tailored to capture Heisenberg-type spin-lattice interactions, while SEGNN accurately models multi-body and high-order spin-lattice coupling. The effectiveness of SpinGNN is demonstrated by its exceptional precision in fitting a high-order spin Hamiltonian and two complex spin-lattice Hamiltonians with great precision. Furthermore, it successfully models the subtle spin-lattice coupling in BiFeO3 and performs large-scale spin-lattice dynamics simulations, predicting its antiferromagnetic ground state, magnetic phase transition, and domain wall energy landscape with high accuracy. Our study broadens the scope of graph neural network potentials to magnetic systems, serving as a foundation for carrying out large-scale spin-lattice dynamic simulations of such systems.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 14 of its 87 references that are in the map. First posted on arXiv 6 Mar 2022.

Builds on

14 of 87 references.

References · 14

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2 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 2.

9 papers.

  1. 2025A practical guide to machine learning interatomic potentials -- Status and futurecited by 24
  2. 2025AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysiscited by 4
  3. 2024Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learningcited by 1
  4. 2024Do Graph Neural Networks Work for High Entropy Alloys?cited by 0
  5. 2024SpinMultiNet: Neural Network Potential Incorporating Spin Degrees of Freedom with Multi-Task Learningcited by 0
  6. 2025Renormalization-Inspired Effective Field Neural Networks for Scalable Modeling of Classical and Quantum Many-Body Systemscited by 0
  7. 2025Learning Magnetic Order Classification from Large-Scale Materials Databasescited by 0
  8. 2026Magnetic HIP-NN for spin dynamics in disordered itinerant magnetscited by 0
  9. 2026Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnetscited by 0