1. Molecular Simulations of Materials in Extreme Conditions
Warm dense matter (WDM) exists in the interior of giant planets in astrophysics and inertial confinement fusion. It is challenging to model WDM since partially degenerate electrons strongly interact with ions. In this regard, a quantum mechanics description of the electrons and ions in WDM is needed. Due to the lack of sufficient experimental data of WDM, first-principles computational methods have attracted increasing attention. Therefore, quantum-mechanics-based first-principles methods are ideal for studying WDM. We focus on developing new methods to study WDM. For example, we have combined the deep potential molecular dynamics method with the density functional theory to study WDM . In addition, we have developed stochastic density functional theory to tackle the electronic structures of WDM .
2. Liquid Water and Water Ions
Water is one of the upmost important material for life and technology. We utilize state-of-the-art first-principles molecular dynamics to study liquid water and ions (hydronium and hydroxide ). We also applied the recently proposed SCAN functional, which is a form of meta-GGA functional that satisfies all 17 known constraints, to study water molecules and found excellent agreement between simulation and experimental results . We combined first-principles molecular dynamics with the deep potential molecular dynamics and achieved some interesting results . Recently, we continue this exciting research towards simulations of water ions including hydronium and hydroxide , as well as Ca2+ and Mg2+ . We also developed machine-learning-assisted electronic structure methods such as DeePKS  to facilitate the study of liquid water and salt water.
 Wenfei Li#, Qi Ou#, Yixiao Chen, Yu Cao, Renxi Liu, Chunyi Zhang, Daye Zheng, Chun Cai, Xifan Wu, Han Wang, Mohan Chen, Linfeng Zhang*, "DeePKS as a bridge between expensive quantum mechanics models and machine learning potentials," J. Phys. Chem. A, 126, 9154 (2022).
 Chunyi Zhang, Fujie Tang, Mohan Chen, Linfeng Zhang, Diana Y. Qiu, John P. Perdew, Michael L. Klein, and Xifan Wu, "Modeling liquid water by climbing up Jacob's ladder in density functional theory facilitated by using deep neural network potentials," J. Phys. Chem. B 125, 11444 (2021).