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 [1][2][3]. In addition, we have developed stochastic density functional theory to tackle the electronic structures of WDM [4].
[4] Qianrui Liu and Mohan Chen*, "Plane-wave-based stochastic-deterministic density functional theory for extended systems," Phys. Rev. B, 106, 125132 (2022). arxiv
[3] Qianrui Liu, Junyi Li, Mohan Chen*, "Thermal transport by electrons and ions in warm dense Aluminum: A combined density functional theory and deep potential study," MRE 6, 026902 (2021). arxiv
[2] Yuzhi Zhang, Chang Gao, Qianrui Liu, Linfeng Zhang, Han Wang, and Mohan Chen*, "Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics," Physics of Plasmas, 27, 122704 (2020). arxiv
[1] Qianrui Liu, Denghui Lu, Mohan Chen*, "Structure and dynamics of warm dense aluminum: a molecular dynamics study with density functional theory and deep potential," J. Phys.: Condens. Matter, 32, 144002 (2020). (2019 Emerging Leaders Special Issue).
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 [3]). 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 [1]. We combined first-principles molecular dynamics with the deep potential molecular dynamics and achieved some interesting results [2][4][5]. Recently, we continue this exciting research towards simulations of water ions including hydronium and hydroxide [6], as well as Ca2+ and Mg2+ [8]. We also developed machine-learning-assisted electronic structure methods such as DeePKS [7] to facilitate the study of liquid water and salt water.
[8] Jianchuan Liu, Renxi Liu, Yu Cao, Mohan Chen*, "Solvation structure of Calcium and Magnesium ions in water with the presence of hydroxide: a study by deep potential molecular dynamics," Phys. Chem. Chem. Phys., in press (2023).
[7] 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).
[6] Renxi Liu, Chunyi Zhang, Xinyuan Liang, Jianchuan Liu, Xifan Wu, and Mohan Chen*, "Structural and dynamic properties of solvated hydroxide and hydronium ions in water from ab Initio modeling," J. Chem. Phys., 157, 024503 (2022). arxiv
[5] 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).
[4] Jianhang Xu#, Chunyi Zhang#, Linfeng Zhang, Mohan Chen, Biswajit Santra, Xifan Wu, "Isotope effects on structural and electronic properties of liquid water via deep potential molecular dynamics based on SCAN functional," Phys. Rev. B, 102, 214113 (2020).
[3] Shanshan Yang#, Mohan Chen#, Yudan Su, Jianhang Xu, Xifan Wu, and Chuanshan Tian ,"Stabilization of hydroxide ion at interface of hydrophobic monolayer on water via reduced proton transfer," Phys. Rev. Lett., 125, 156803 (2020).
[2] Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, and Roberto Car, "Deep neural network for the dielectric response of insulators," Phys. Rev. B (Rapid Comm.), 102, 041121 (2020). (Editor's Suggestion)
[1] Jianhang Xu, Mohan Chen, Cui Zhang, and Xifan Wu*, "First-principles studies of the infrared spectra in liquid water from a systematically improved description of H-bond network," Phys. Rev. B, 99, 205123 (2019).