most citedPre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models

9 citations · 9 across the 4 of their papers we have counts for

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cond-mat.mtrl-sci2025

Chemical-space completeness through iterative crystal-structure generation and model adaptation

Fengyu Xie, Ruoyu Wang, Taoyuze Lv +3

The emergence of deep learning has brought large-scale exploration of crystalline materials closer to practical realization. Yet, universal atomistic models face a practical trade-…

cond-mat.mtrl-sci2025

Uncovering coupled ionic-polaronic dynamics and interfacial enhancement in LiFePO

Fengyu Xie, Yuxiang Gao, Ruoyu Wang +1

Understanding and controlling coupled ionic-polaronic dynamics is crucial for optimizing electrochemical performance in battery materials. However, studying such coupled dynamics r…

cond-mat.mtrl-sci2025★ 9 cited

Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models

Ruoyu Wang, Yuxiang Gao, Hongyu Wu +1

Universal force fields generalizable across the periodic table represent a new trend in computational materials science. However, the applications of universal force fields in mate…