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

52 citations · 135 across the 21 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

hep-ph2024

In-depth Study of Spin- and Momentum-Dependent Interaction Potentials Between Two Spin-1/2 Fermions Mediated by Light Spin-0 Particles

Yang Zhong, Zhi-Hui Guo, Hai-Qing Zhou

We present a calculation by including the relativistic and off-shell contributions to the interaction potentials between two spin-1/2 fermions mediated by the exchange of light spi…

physics.comp-ph2024★ 1 cited

Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks

Rui Wang, Hongyu Yu, Yang Zhong +1

The application of machine learning methodologies for predicting properties within materials science has garnered significant attention. Among recent advancements, Kolmogorov-Arnol…

physics.comp-ph2024★ 3 cited

Advancing Nonadiabatic Molecular Dynamics Simulations for Solids: Achieving Supreme Accuracy and Efficiency with Machine Learning

Changwei Zhang, Yang Zhong, Zhi-Guo Tao +7

Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framewo…

cond-mat.mtrl-sci2024★ 1 cited

Identifying Direct Bandgap Silicon Structures with High-throughput Search and Machine Learning Methods

Rui Wang, Hongyu Yu, Yang Zhong +1

Utilizations of silicon-based luminescent devices are restricted by the indirect-gap nature of diamond silicon. In this study, the high-throughput method is employed to expedite di…

physics.comp-ph2024★ 42 cited

Universal Machine Learning Kohn-Sham Hamiltonian for Materials

Yang Zhong, Hongyu Yu, Jihui Yang +3

While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persi…