7 papers
Understanding the Density Maximum of Water with Machine Learned Potentials
Yizhi Song, Renxi Liu, Chunyi Zhang +5
After melting, at ambient pressure, the density of water continues to increase with temperature until it reaches a maximum around 4 °C. For nearly a century, this phenomenon has b…
fix pimd/langevin: An Efficient Implementation of Path Integral Molecular Dynamics in LAMMPS
Yifan Li, Axel Gomez, Kehan Cai +10
Path integral molecular dynamics (PIMD), which maps a quantum particle onto a fictitious classical system of ring polymers and propagates the "beads" of this extended classical sys…
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…
Ab Initio Melting Properties of Water and Ice from Machine Learning Potentials
Yifan Li, Bingjia Yang, Chunyi Zhang +5
Liquid water exhibits several important anomalous properties in the vicinity of the melting temperature () of ice Ih, including a higher density than ice and a dens…
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)…