Assessment of First-Principles Methods in Modeling the Melting Properties of Water
Princeton University（普林斯顿大学）；Eastern Institute of Technology（东方理工学院）；CIC nanoGUNE BRTA（nanoGUNE应用研究中心）；Ikerbasque, Basque Foundation for Science（伊克尔巴斯科克，巴斯克科学基金会）
Yifan Li, Bingjia Yang, Chunyi Zhang, Axel Gomez, Pinchen Xie, Yixiao Chen, Pablo M. Piaggi, Roberto Car
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 these first-principles data widen the range of accessible properties. However, the capabilities of different first-principles methods are not yet fully understood due to the lack of systematic benchmarks, the underestimation of the uncertainties introduced by MLPs, and the neglect of nuclear quantum effects (NQEs). Here, we systematically assess first-principles methods by calculating key melting properties using path integral molecular dynamics (PIMD) driven by Deep Potential (DP) models trained on data from density functional theory (DFT) with SCAN, revPBE0-D3, SCAN0 and revPBE-D3 functionals, as well as from the MB-pol potential. We find that MB-pol is in qualitatively good agreement with the experiment in all properties tested, whereas the four DFT functionals incorrectly predict that NQEs increase the melting temperature. SCAN and SCAN0 slightly underestimate the density change between water and ice upon melting, but revPBE-D3 and revPBE0-D3 severely underestimate it. Moreover, SCAN and SCAN0 correctly predict that the maximum liquid density occurs at a temperature higher than the melting point, while revPBE-D3 and revPBE0-D3 predict the opposite behavior. Our results highlight limitations in widely used first-principles methods and call for a reassessment of their predictive power in aqueous systems.
A study of the influence of isotopic substitution on the melting point and temperature of maximum density of water by means of path integral simulations of rigid models
Understanding the Density Maximum of Water with Machine Learned Potentials
Temple University（天普大学）；Eastern Institute of Technology（宁波东方理工大学）；Princeton University（普林斯顿大学）；Peking University（北京大学）；Institute for Computational Molecular Science, Temple University（天普大学计算分子科学研究所）
