Montero de Hijes Pablo, Dellago Christoph, Jinnouchi Ryosuke, Kresse Georg
University of Vienna, Faculty of Physics, Kolingasse 14, A-1090 Vienna, Austria.
University of Vienna, Faculty of Earth Sciences, Geography and Astronomy, Josef-Holaubuek-Platz 2, 1090 Vienna, Austria.
J Chem Phys. 2024 Oct 7;161(13). doi: 10.1063/5.0227514.
We investigate the density isobar of water and the melting temperature of ice using six different density functionals. Machine-learning potentials are employed to ensure computational affordability. Our findings reveal significant discrepancies between various base functionals. Notably, even the choice of damping can result in substantial differences. Overall, the outcomes obtained through density functional theory are not entirely satisfactory across most utilized functionals. All functionals exhibit significant deviations either in the melting temperature or equilibrium volume, with most of them even predicting an incorrect volume difference between ice and water. Our heuristic analysis indicates that a hybrid functional with 25% exact exchange and van der Waals damping averaged between zero and Becke-Johnson dampings yields the closest agreement with experimental data. This study underscores the necessity for further enhancements in the treatment of van der Waals interactions and, more broadly, density functional theory to enable accurate quantitative predictions for molecular liquids.
我们使用六种不同的密度泛函研究了水的密度等压线和冰的熔化温度。采用机器学习势以确保计算的可承受性。我们的研究结果揭示了各种基函数之间存在显著差异。值得注意的是,即使是阻尼的选择也会导致很大的差异。总体而言,在大多数使用的泛函中,通过密度泛函理论获得的结果并不完全令人满意。所有泛函在熔化温度或平衡体积方面都表现出显著偏差,其中大多数甚至预测了冰和水之间不正确的体积差。我们的启发式分析表明,具有25%精确交换和介于零阻尼与Becke-Johnson阻尼之间的范德华阻尼的混合泛函与实验数据的一致性最为接近。这项研究强调了在范德华相互作用的处理方面,更广泛地说,在密度泛函理论方面进一步改进的必要性,以便能够对分子液体进行准确的定量预测。