Quantum-accurate magneto-elastic predictions with classical spin-lattice dynamics

Abstract

A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP. Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed. Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations. We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP for α-iron. The combined potential energy surface yields excellent agreement with first-principles magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus, magnetization, and specific heat across the ferromagnetic-paramagnetic phase transition.

arXiv comment15 pages, 6 figures, 2 tables

References in corpus (25)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• Ab-initio calculations of exchange interactions, spin-wave stiffness constants, and Curie temperatures of Fe, Co, and Ni
• Probing the ultimate limits of metal plasticity
• Hierarchical modeling of molecular energies using a deep neural network
• Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron
• Extending the Accuracy of the SNAP Interatomic Potential Form
• Quantitative simulation of temperature dependent magnetization dynamics and equilibrium properties of elemental ferromagnets
• Massively parallel symplectic algorithm for coupled magnetic spin dynamics and molecular dynamics
• Atomic cluster expansion of scalar, vectorial and tensorial properties and including magnetism and charge transfer
• Data-driven Material Models for Atomistic Simulation
• Comparison of first-principles methods to extract magnetic parameters in ultra-thin films: Co/Pt(111)
• Thermoelastic properties of $\alpha$-iron from first-principles
• Dynamic Simulation of Structural Phase Transitions in Magnetic Iron
• Realistic finite temperature simulations of magnetic systems using quantum statistics
• The Dynamics of Magnetism in Fe-Cr Alloys with Cr Clustering
• Size and temperature dependent magnetization of iron nanoclusters
• Atomistic Simulation of Phonon and Magnon Thermal Transport across the Ferro-Paramagnetic Transition
• On the magnetoelastic and magnetoelectric couplings across the antiferromagnetic transition in multiferroic BiFeO3
• Spin orientation and magnetostriction of Tb${}_{1-x}$Dy${}_x$Fe${}_2$ from first principles
• Effect of Non-Heisenberg Magnetic Interactions on Defects in Ferromagnetic Iron
• Dynamic magnetostriction for antiferromagnets

Cited by in corpus (13)

• A practical guide to machine learning interatomic potentials -- Status and future
• Spin-Dependent Graph Neural Network Potential for Magnetic Materials
• A spectral-neighbour representation for vector fields: machine-learning potentials including spin
• Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron
• Atomistic simulations of magnetoelastic effects on sound velocity
• Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter
• General time-reversal equivariant neural network potential for magnetic materials
• A Self-Adaptive First-Principles Approach for Magnetic Excited States
• Fitting to magnetic forces improves the reliability of magnetic Moment Tensor Potentials
• Calculating spin-lattice interactions in ferro- and antiferromagnets: the role of symmetry, dimension and frustration
• A High-Performance Implementation of Atomistic Spin Dynamics Simulations on x86 CPUs
• Spin-lattice couplings in $3d$ ferromagnets: analysis from first-principles
• Radiation damage and phase stability of Al${}_x$CrCuFeNi${}_y$ alloys using a machine-learned interatomic potential