most citedFine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications

2 citations · 3 across the 3 of their papers we have counts for

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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…