General time-reversal equivariant neural network potential for magnetic materials

• URL: http://arxiv.org/abs/2211.11403v3
• Date: Mon, 8 Jan 2024 12:45:12 GMT
• Title: General time-reversal equivariant neural network potential for magnetic materials
• Authors: Hongyu Yu, Boyu Liu, Yang Zhong, Liangliang Hong, Junyi Ji, Changsong Xu, Xingao Gong, Hongjun Xiang
• Abstract summary: This study introduces time-reversal E(3)-equivariant neural network and SpinGNN++ framework for constructing a comprehensive interatomic potential for magnetic systems. SpinGNN++ integrates spin equivariant neural network with explicit spin-lattice terms, including Heisenberg, Dzyaloshinskii-Moriya, Kitaev, single-ion anisotropy, and biquadratic interactions. SpinGNN++ identifies a new ferrimagnetic state as the ground magnetic state for monolayer CrTe2.
• Score: 5.334610924852583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study introduces time-reversal E(3)-equivariant neural network and SpinGNN++ framework for constructing a comprehensive interatomic potential for magnetic systems, encompassing spin-orbit coupling and noncollinear magnetic moments. SpinGNN++ integrates multitask spin equivariant neural network with explicit spin-lattice terms, including Heisenberg, Dzyaloshinskii-Moriya, Kitaev, single-ion anisotropy, and biquadratic interactions, and employs time-reversal equivariant neural network to learn high-order spin-lattice interactions using time-reversal E(3)-equivariant convolutions. To validate SpinGNN++, a complex magnetic model dataset is introduced as a benchmark and employed to demonstrate its capabilities. SpinGNN++ provides accurate descriptions of the complex spin-lattice coupling in monolayer CrI$_3$ and CrTe$_2$, achieving sub-meV errors. Importantly, it facilitates large-scale parallel spin-lattice dynamics, thereby enabling the exploration of associated properties, including the magnetic ground state and phase transition. Remarkably, SpinGNN++ identifies a new ferrimagnetic state as the ground magnetic state for monolayer CrTe2, thereby enriching its phase diagram and providing deeper insights into the distinct magnetic signals observed in various experiments.

Related papers

• SpinMultiNet: Neural Network Potential Incorporating Spin Degrees of Freedom with Multi-Task Learning [0.0]
  This study introduces SpinMultiNet, a novel NNP model that integrates spin degrees of freedom through multi-task learning. SpinMultiNet achieves accurate predictions without relying on correct spin values obtained from DFT calculations. validation on a dataset of transition metal oxides demonstrates the high predictive accuracy of SpinMultiNet.
  arXiv  Detail & Related papers  (2024-09-05T05:13:28Z)
• Neural Networks as Spin Models: From Glass to Hidden Order Through Training [0.0]
  We explore a one-to-one correspondence between a neural network (NN) and a statistical mechanical spin model. We study the magnetic phases and the melting transition temperature as training progresses.
  arXiv  Detail & Related papers  (2024-08-12T18:01:04Z)
• High-accuracy variational Monte Carlo for frustrated magnets with deep neural networks [1.2891210250935146]
  We show that neural quantum states based on very deep (4--16-layered) neural networks can outperform state-of-the-art variational approaches on highly frustrated quantum magnets. We focus on group convolutional neural networks (GCNNs) that allow us to impose space-group symmetries on our ans"atze.
  arXiv  Detail & Related papers  (2022-11-14T20:51:19Z)
• Observation of anisotropy-independent magnetization dynamics in spatially disordered Heisenberg spin systems [0.0]
  We experimentally observe robust features in the magnetization relaxation dynamics of disordered Heisenberg XX-, XXZ- and Ising Hamiltonians. In numerical simulations of small systems, we show that these pairs of spins constitute approximate local integrals of motion.
  arXiv  Detail & Related papers  (2022-09-16T17:44:49Z)
• Probing dynamics of a two-dimensional dipolar spin ensemble using single qubit sensor [62.997667081978825]
  We experimentally investigate individual spin dynamics in a two-dimensional ensemble of electron spins on the surface of a diamond crystal. We show that this anomalously slow relaxation rate is due to the presence of strong dynamical disorder. Our work paves the way towards microscopic study and control of quantum thermalization in strongly interacting disordered spin ensembles.
  arXiv  Detail & Related papers  (2022-07-21T18:00:17Z)
• Spreading of a local excitation in a Quantum Hierarchical Model [62.997667081978825]
  We study the dynamics of the quantum Dyson hierarchical model in its paramagnetic phase. An initial state made by a local excitation of the paramagnetic ground state is considered. A localization mechanism is found and the excitation remains close to its initial position at arbitrary times.
  arXiv  Detail & Related papers  (2022-07-14T10:05:20Z)
• Spin-Dependent Graph Neural Network Potential for Magnetic Materials [5.775111970429336]
  SpinGNN is a spin-dependent interatomic potential approach that employs the graph neural network (GNN) to describe magnetic systems. The effectiveness of SpinGNN is demonstrated by its exceptional precision in fitting a high-order spin Hamiltonian and two complex spin-lattice Hamiltonians.
  arXiv  Detail & Related papers  (2022-03-06T01:54:50Z)
• Anisotropic electron-nuclear interactions in a rotating quantum spin bath [55.41644538483948]
  Spin-bath interactions are strongly anisotropic, and rapid physical rotation has long been used in solid-state nuclear magnetic resonance. We show that the interaction between electron spins of nitrogen-vacancy centers and a bath of $13$C nuclear spins introduces decoherence into the system. Our findings offer new insights into the use of physical rotation for quantum control with implications for quantum systems having motional and rotational degrees of freedom that are not fixed.
  arXiv  Detail & Related papers  (2021-05-16T06:15:00Z)
• Controlled coherent dynamics of [VO(TPP)], a prototype molecular nuclear qudit with an electronic ancilla [50.002949299918136]
  We show that [VO(TPP)] (vanadyl tetraphenylporphyrinate) is a promising system suitable to implement quantum computation algorithms. It embeds an electronic spin 1/2 coupled through hyperfine interaction to a nuclear spin 7/2, both characterized by remarkable coherence.
  arXiv  Detail & Related papers  (2021-03-15T21:38:41Z)
• Superradiant phase transition in complex networks [62.997667081978825]
  We consider a superradiant phase transition problem for the Dicke-Ising model. We examine regular, random, and scale-free network structures.
  arXiv  Detail & Related papers  (2020-12-05T17:40:53Z)
• Spin Entanglement and Magnetic Competition via Long-range Interactions in Spinor Quantum Optical Lattices [62.997667081978825]
  We study the effects of cavity mediated long range magnetic interactions and optical lattices in ultracold matter. We find that global interactions modify the underlying magnetic character of the system while introducing competition scenarios. These allow new alternatives toward the design of robust mechanisms for quantum information purposes.
  arXiv  Detail & Related papers  (2020-11-16T08:03:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.