17 citations · 42 across the 9 of their papers we have counts for
5 papers · 1 filter
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 be…
A Deep Learning Framework for the Electronic Structure of Water: Towards a Universal Model
Xinyuan Liang, Renxi Liu, Mohan Chen
Accurately modeling the electronic structure of water across scales, from individual molecules to bulk liquid, remains a grand challenge. Traditional computational methods face a c…
Characterization of the Hydrogen-Bond Network in High-Pressure Water by Deep Potential Molecular Dynamics
Renxi Liu, Mohan Chen
The hydrogen-bond (H-bond) network of high-pressure water is investigated by neural-network-based molecular dynamics (MD) simulations with the first-principles accuracy. The static…
DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials
Wenfei Li, Qi Ou, Yixiao Chen +9
Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanica…
Structural and Dynamic Properties of Solvated Hydroxide and Hydronium Ions in Water from Ab Initio Modeling
Renxi Liu, Chunyi Zhang, Xinyuan Liang +3
Predicting the asymmetric structure and dynamics of solvated hydroxide and hydronium in water has been a challenging task from ab initio molecular dynamics (AIMD). The difficulty m…