activity
20102025
most citedAb initio theory and modeling of water

502 citations · 1.9k across the 35 of their papers we have counts for

collaborators

35 papers

physics.plasm-ph2025

Thermal and Electrical Conductivities of Aluminum Up to 1000 eV: A First-Principles Prediction

Qianrui Liu, Xiantu He, Mohan Chen

Accurate prediction of the thermal and electrical conductivities of materials under extremely high temperatures is essential in high-energy-density physics. These properties govern…

cond-mat.mtrl-sci2023

Implementation of the SCAN Exchange-Correlation Functional with Numerical Atomic Orbitals

Renxi Liu, Daye Zheng, Xinyuan Liang +3

Kohn-Sham density functional theory (DFT) is nowadays widely used for electronic structure theory simulations, and the accuracy and efficiency of DFT rely on approximations of the…

physics.comp-ph2023★ 8 cited

Truncated Non-Local Kinetic Energy Density Functionals for Simple Metals and Silicon

Liang Sun, Yuanbo Li, Mohan Chen

Adopting an accurate kinetic energy density functional (KEDF) to characterize the noninteracting kinetic energy within the framework of orbital-free density functional theory (OFDF…

physics.chem-ph2023

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…

cond-mat.mtrl-sci2022

Disordered Hyperuniform Solid State Materials

Duyu Chen, Houlong Zhuang, Mohan Chen +3

Disordered hyperuniform (DHU) states are recently discovered exotic states of condensed matter. DHU systems are similar to liquids or glasses in that they are statistically isotrop…

cond-mat.mtrl-sci2022★ 21 cited

Modeling the High-Pressure Solid and Liquid Phases of Tin from Deep Potentials with ab initio Accuracy

Tao Chen, Fengbo Yuan, Jianchuan Liu +4

Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-ba…