most citedGPU-MetaD: Full-Life-Cycle GPU Accelerated Metadynamics with Machine Learning Potentials

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physics.comp-ph2026

Cartesian tensor equivariant machine-learning force field for spin-dependent atomistic simulations

Junjie Wang, Yijie Zhu, Zhongwei Zhang +5

Magnetic materials exhibit an intricate coupling between atomic structure and spin degrees of freedom, posing a fundamental challenge for atomistic simulations across experimentall…

physics.comp-ph2025★ 1 cited

GPU-MetaD: Full-Life-Cycle GPU Accelerated Metadynamics with Machine Learning Potentials

Haoting Zhang, Qiuhan Jia, Zhennan Zhang +6

Large-scale molecular dynamics simulations with high accuracy have been increasingly popular for their capability to bridge the gap between atomistic modeling and mesoscale phenome…