Spin-Dependent Graph Neural Network Potential for Magnetic Materials

Abstract

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.

arXiv comment28 pages, 4 figures

References in corpus (32)

• A linear response approach to the calculation of the effective interaction parameters in the LDA+U method
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• On representing chemical environments
• Exchange Monte Carlo Method and Application to Spin Glass Simulations
• Machine learning and the physical sciences
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• SchNet - a deep learning architecture for molecules and materials
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• First-principles study of spontaneous polarization in multiferroic BiFeO${}_3$
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
• Atomistic Line Graph Neural Network for Improved Materials Property Predictions
• Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
• A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
• Force and heat current formulas for many-body potentials in molecular dynamics simulation with applications to thermal conductivity calculations
• First-principles study of ferroelectric domain walls in multiferroic bismuth ferrite
• Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation
• Massively parallel symplectic algorithm for coupled magnetic spin dynamics and molecular dynamics
• Stable and fast semi-implicit integration of the stochastic Landau-Lifshitz equation
• Spectral Decomposition of Thermal Conductivity: Comparing Velocity Decomposition Methods in Homogeneous Molecular Dynamics Simulations
• High-Dimensional Neural Network Potentials for Magnetic Systems Using Spin-Dependent Atom-Centered Symmetry Functions
• Domain walls in a perovskite oxide with two primary structural order parameters: first-principles study of BiFeO${}_3$
• Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics
• 180 degree Ferroelectric Stripe Nanodomains in BiFeO3 Thin Films
• Ab initio calculation of real solids via neural network ansatz
• Magnetic Interactions in BiFeO${}_3$: a First-Principles Study
• Quantum-accurate magneto-elastic predictions with classical spin-lattice dynamics
• Inverse design of glass structure with deep graph neural networks
• Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions
• Complex Spin Hamiltonian Represented by Artificial Neural Network

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