3 citations · 5 across the 3 of their papers we have counts for
3 papers
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…
physics.comp-ph2024★ 3 cited
RBMD: A molecular dynamics package enabling to simulate 10 million all-atom particles in a single graphics processing unit
Weihang Gao, Teng Zhao, Yongfa Guo +9
This paper introduces a random-batch molecular dynamics (RBMD) package for fast simulations of particle systems at the nano/micro scale. Different from existing packages, the RBMD…