activity
20222026
most citedSpin-Dependent Graph Neural Network Potential for Magnetic Materials

52 citations · 75 across the 7 of their papers we have counts for

collaborators

7 papers

cond-mat.mtrl-sci2026

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

Xianxi Zhang, Hongyu Yu, Liangliang Hong +1

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…

cond-mat.mtrl-sci2025★ 6 cited

Generation of Pure Spin Current with Insulating Antiferromagnetic Materials

Yingwei Chen, Junyi Ji, Liangliang Hong +2

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 c…

cond-mat.mtrl-sci2023

Towards Ultimate Memory with Single-Molecule Multiferroics

Yali Yang, Liangliang Hong, Laurent Bellaiche +1

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 promis…

cond-mat.mtrl-sci2023★ 1 cited

Evaluating Gilbert Damping in Magnetic Insulators from First Principles

Liangliang Hong, Changsong Xu, Hongjun Xiang

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 dampi…

cond-mat.mtrl-sci2022★ 4 cited

Capturing long-range interaction with reciprocal space neural network

Hongyu Yu, Liangliang Hong, Shiyou Chen +2

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 dy…

cond-mat.mtrl-sci2022★ 12 cited

General time-reversal equivariant neural network potential for magnetic materials

Hongyu Yu, Boyu Liu, Yang Zhong +5

This study introduces time-reversal E(3)-equivariant neural network and SpinGNN++ framework for constructing a comprehensive interatomic potential for magnetic systems, encompassin…

physics.comp-ph2022★ 52 cited

Spin-Dependent Graph Neural Network Potential for Magnetic Materials

Hongyu Yu, Yang Zhong, Liangliang Hong +4

The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals. However, creating interatomic potenti…