2 citations · 2 across the 3 of their papers we have counts for
3 papers
physics.comp-ph2026
RBMD 2.0: Random batch molecular dynamics package for large-scale simulations on multi-GPU architectures
Qi Zhou, Yongfa Guo, Jincheng Zhong +3
Large-scale molecular dynamics simulations of particle systems on multi-GPU architectures are often constrained by the computational and communication costs of nonbonded force eval…
cond-mat.mtrl-sci2026
An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces
Xiaoqing Liu, Xinyu Yu, Yangshuai Wang +6
Atomistic modeling of solid-solid battery interfaces is essential for understanding electro-chemo-mechanical coupling, but the complex interfacial chemistry and heterogeneous envir…
physics.comp-ph2025★ 2 cited
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Xiaoqing Liu, Kehan Zeng, Zedong Luo +3
Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated broad applicability across diverse atomistic systems but often require fine-tuning to achieve task-spec…