2 citations · 6 across the 8 of their papers we have counts for
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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…
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,…
Simulations of dielectric permittivity of water by Machine Learned Potentials with long-range Coulombic interactions
Kehan Cai, Chunyi Zhang, Xifan Wu
The dielectric permittivity of liquid water is a fundamental property that underlies its distinctive behaviors in numerious physical, biological, and chemical processes. Within a m…
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)…