arXiv subjects

Liangliang Hong

Publications and source records attributed to Liangliang Hong.

7 recordsLinked to original sources

arXiv2026

A Fully Ab-Initio Spin-Lattice Dynamics Framework for Magnetic Materials

Coupled spin-lattice dynamics (SLD) underlie a wide range of magnetic phenomena, yet a unified first-principles framework that propagates both degrees of freedom without empirical parameterization has remained elusive. We present a fully ab initio SLD approach integrated into VASP, in which interatomic forces and effective magnetic fields are obtained at each time step from self-consistent constrained-moment density-functional calculations. The method is validated on four materials spanning ferromagnetic, non-collinear, and geometrically frustrated orders, recovering the correct magnetic ground state in every case from random initial conditions. SLD trajectories also provide physically correlated training data for magnetic machine-learning potentials, as demonstrated for BiFeO$_3$ by a reduction of up to approximately one order of magnitude in energy MAE over training on randomized spin configurations. This framework opens a practical first-principles route to finite-temperature spin-lattice coupled phenomena in magnetic materials.

cond-mat.mtrl-sci↗

arXiv2025

Generation of Pure Spin Current with Insulating Antiferromagnetic Materials

The generation of pure spin currents is critical for low-dissipation spintronic applications, yet existing methods relying on spin-orbit coupling or ferromagnetic interfaces face challenges in material compatibility and operational robustness. We propose a paradigm-shifting approach to generate symmetry-protected pure spin currents by applying mechanical stress on insulating antiferromagnetic materials, i.e., the pure piezospintronic effect. We first classify magnetic point groups enabling pure piezospintronic effects. A novel first-principles method is developed to compute the spin dipole moments and coefficients of the piezospintronic effect. Integrating these methodologies with high-throughput screening, we identify FeOOH, Cr2O3 and NaMnX (X=As, Bi, P, Sb) with significant pure piezospintronic effects. Interestingly, we reveal that the ionic displacement contribution dominates the piezospintronic effect, in contrast to the piezoelectric effect. Our study not only provides first-principles approach for investigating spin dipole moment related phenomena (e.g., ferrotoroidicity, fractional quantum spin dipole moment, piezospintronics), but also provide promising piezospintronic materials for experimental verification and industrial applications.

cond-mat.mtrl-sci↗

arXiv2024

General time-reversal equivariant neural network potential for magnetic materials

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.

cond-mat.mtrl-sci↗

arXiv2023

Towards Ultimate Memory with Single-Molecule Multiferroics

The demand for high-density storage is urgent in the current era of data explosion. Recently, several single-molecule (-atom) magnets/ferroelectrics have been reported to be promising candidates for high-density storage. As another promising candidate, single-molecule multiferroics are not only small but also possess ferroelectric and magnetic orderings, which can sometimes be strongly coupled and used as data storages to realize the combination of electric writing and magnetic reading. However, they have been rarely proposed, and never been experimentally reported. Here, by building Hamiltonian models, we propose a new model of single-molecule multiferroic in which electric dipoles and magnetic moments are parallel and can rotate with the rotation of the single molecule. Furthermore, with performing spin-lattice dynamics simulations, we reveal the conditions (e.g., large enough single-ion anisotropy and appropriate electric field) under which the new single-molecule multiferroic can arise. Based on this model, as well as first-principles calculations, a realistic example Co(NH3)4N@SWCNT is constructed and numerically confirmed to demonstrate the feasibility of the new single-molecule multiferroic model. Our work not only sheds light on the discovery of single-molecule multiferroics but also provides a new guideline to design multifunctional materials for ultimate memory devices.

cond-mat.mtrl-sci↗

arXiv2023

Evaluating Gilbert Damping in Magnetic Insulators from First Principles

Magnetic damping has a significant impact on the performance of various magnetic and spintronic devices, making it a long-standing focus of research. The strength of magnetic damping is usually quantified by the Gilbert damping constant in the Landau-Lifshitz-Gilbert equation. Here we propose a first-principles based approach to evaluate the Gilbert damping constant contributed by spin-lattice coupling in magnetic insulators. The approach involves effective Hamiltonian models and spin-lattice dynamics simulations. As a case study, we applied our method to Y$_3$Fe$_5$O$_{12}$, MnFe$_2$O$_4$ and Cr$_2$O$_3$. Their damping constants were calculated to be $0.8\times10^{-4}$, $0.2\times10^{-4}$, $2.2\times 10^{-4}$, respectively at a low temperature. The results for Y$_3$Fe$_5$O$_{12}$ and Cr$_2$O$_3$ are in good agreement with experimental measurements, while the discrepancy in MnFe$_2$O$_4$ can be attributed to the inhomogeneity and small band gap in real samples. The stronger damping observed in Cr$_2$O$_3$, compared to Y$_3$Fe$_5$O$_{12}$, essentially results from its stronger spin-lattice coupling. In addition, we confirmed a proportional relationship between damping constants and the temperature difference of subsystems, which had been reported in previous studies. These successful applications suggest that our approach serves as a promising candidate for estimating the Gilbert damping constant in magnetic insulators.

cond-mat.mtrl-sci↗

arXiv2023

Spin-Dependent Graph Neural Network Potential for Magnetic Materials

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.

physics.comp-ph↗

arXiv2022

Capturing long-range interaction with reciprocal space neural network

Machine Learning (ML) interatomic models and potentials have been widely employed in simulations of materials. Long-range interactions often dominate in some ionic systems whose dynamics behavior is significantly influenced. However, the long-range effect such as Coulomb and Van der Wales potential is not considered in most ML interatomic potentials. To address this issue, we put forward a method that can take long-range effects into account for most ML local interatomic models with the reciprocal space neural network. The structure information in real space is firstly transformed into reciprocal space and then encoded into a reciprocal space potential or a global descriptor with full atomic interactions. The reciprocal space potential and descriptor keep full invariance of Euclidean symmetry and choice of the cell. Benefiting from the reciprocal-space information, ML interatomic models can be extended to describe the long-range potential including not only Coulomb but any other long-range interaction. A model NaCl system considering Coulomb interaction and the GaxNy system with defects are applied to illustrate the advantage of our approach. At the same time, our approach helps to improve the prediction accuracy of some global properties such as the band gap where the full atomic interaction beyond local atomic environments plays a very important role. In summary, our work has expanded the ability of current ML interatomic models and potentials when dealing with the long-range effect, hence paving a new way for accurate prediction of global properties and large-scale dynamic simulations of systems with defects.

cond-mat.mtrl-sci↗