Complex Spin Hamiltonian Represented by Artificial Neural Network

Abstract

The effective spin Hamiltonian method is widely adopted to simulate and understand the behavior of magnetism. However, the magnetic interactions of some systems, such as itinerant magnets, are too complex to be described by any explicit function, which prevents an accurate description of magnetism in such systems. Here, we put forward a machine learning (ML) approach, applying an artificial neural network (ANN) and a local spin descriptor to develop effective spin potentials for any form of interaction. The constructed Hamiltonians include an explicit Heisenberg part and an implicit non-linear ANN part. Such a method successfully reproduces artificially constructed models and also sufficiently describe the itinerant magnetism of bulk Fe3GeTe2. Our work paves a new way for investigating complex magnetic phenomena (e.g., skyrmions) of magnetic materials.

arXiv comment14 pages, 3 figures

References in corpus (30)

• Layer-dependent Ferromagnetism in a van der Waals Crystal down to the Monolayer Limit
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Gate-tunable Room-temperature Ferromagnetism in Two-dimensional Fe${}_3$GeTe${}_2$
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• Spontaneous Skyrmion Ground States in Magnetic Metals
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Spin Ice State in Frustrated Magnetic Pyrochlore Materials
• Topological Hall effect in the A-phase of MnSi
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Magnetic Monopoles in Spin Ice
• Giant anomalous Hall effect in a ferromagnetic Kagome-lattice semimetal
• Parallel Tempering: Theory, Applications, and New Perspectives
• Spin Liquid Ground State of the $S=1/2$ Kagome Heisenberg Model
• Fractionalized excitations in the spin liquid state of a kagomé lattice antiferromagnet
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• DScribe: Library of Descriptors for Machine Learning in Materials Science
• Machine learning a general purpose interatomic potential for silicon
• Observation of Magnetic Skyrmion Bubbles in a van der Waals ferromagnet Fe3GeTe2
• Néel-type skyrmion in WTe2/Fe3GeTe2 van der Waals heterostructure
• Magnetic structure and phase stability of the van der Waals bonded ferromagnet Fe3-xGeTe2
• Néel-type skyrmions and their current-induced motion in van der Waals ferromagnet-based heterostructures
• Possible Kitaev Quantum Spin Liquid State in 2D Materials with S=3/2
• Higher-order exchange interactions in two-dimensional magnets
• Topological-chiral magnetic interactions driven by emergent orbital magnetism
• Role of higher-order exchange interactions for skyrmion stability
• Giant Biquadratic Exchange in 2D Magnets and its Role in Stabilizing Ferromagnetism of NiCl2 Monolayer
• Deep learning inter-atomic potential model for accurate irradiation damage simulations
• Novel Itinerant Antiferromagnet TiAu
• Atomistic Mechanism Underlying the Si(111)-(7\times7) Surface Reconstruction Revealed by Artificial Neural-network Potential

Cited by in corpus (7)

• A practical guide to machine learning interatomic potentials -- Status and future
• Spin-Dependent Graph Neural Network Potential for Magnetic Materials
• Tuning the magnetic interactions in van der Waals Fe${}_3$GeTe${}_2$ heterostructures: A comparative study of \textit{ab initio} methods
• General time-reversal equivariant neural network potential for magnetic materials
• A Self-Adaptive First-Principles Approach for Magnetic Excited States
• Classification of magnetic order from electronic structure by using machine learning
• Smooth Overlap of Spin Orientations: Machine Learning Exchange Fields for Ab-initio Spin Dynamics