2 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
Beyond Adam: Disentangling Optimizer Effects in the Fine-Tuning of Atomistic Foundation Models
Xiaoqing Liu, Yangshuai Wang, Teng Zhao
Atomistic foundation models constitute a paradigm shift in computational materials science by providing universal machine-learned interatomic potentials with broad transferability…
A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)
Xiaoqing Liu, Kehan Zeng, Yangshuai Wang +1
Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated effectiveness across diverse atomistic systems but often require fine-tuning for task-specific accuracy…
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…
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…