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20242026
most citedTraining Dataset Matters for Machine Learning Interatomic Potentials: Water Simulation as an Example

2 citations · 6 across the 7 of their papers we have counts for

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physics.chem-ph2025★ 2 cited

Assessment of First-Principles Methods in Modeling the Melting Properties of Water

Yifan Li, Bingjia Yang, Chunyi Zhang +5

First-principles simulations have played a crucial role in deepening our understanding of the thermodynamic properties of water, and machine learning potentials (MLPs) trained on t…

physics.chem-ph2025★ 2 cited

Training Dataset Matters for Machine Learning Interatomic Potentials: Water Simulation as an Example

Yifan Li, Bingjia Yang, Chunyi Zhang +5

Accurate predictions of machine learning interatomic potentials (MLIPs) rely on training datasets covering relevant thermodynamic conditions and phases. Using water as an example,…

physics.comp-ph2025

Spectral Similarity Masks Structural Diversity at Hydrophobic Water Interfaces

Yong Wang, Yifan Li, Linhan Du +5

The air-water and graphene-water interfaces represent quintessential examples of the liquid-gas and liquid-solid boundaries, respectively. While the sum-frequency generation (SFG)…