Department of Physics, Temple University, Philadelphia, PA 19122.
Department of Chemistry, Princeton University, Princeton, NJ 08544.
Proc Natl Acad Sci U S A. 2017 Oct 10;114(41):10846-10851. doi: 10.1073/pnas.1712499114. Epub 2017 Sep 25.
Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio approach, relying on the strongly constrained and appropriately normed (SCAN) density functional, provides such a description of water. SCAN accurately describes the balance among covalent bonds, hydrogen bonds, and van der Waals interactions that dictates the structure and dynamics of liquid water. Notably, SCAN captures the density difference between water and ice I at ambient conditions, as well as many important structural, electronic, and dynamic properties of liquid water. These successful predictions of the versatile SCAN functional open the gates to study complex processes in aqueous phase chemistry and the interactions of water with other materials in an efficient, accurate, and predictive, ab initio manner.
水对生命和技术至关重要。然而,真正能够对水进行预测的从头算模型一直困扰着科学家们。我们证明,一种完全基于从头算的方法,依赖于受到强约束和适当归一化的(SCAN)密度泛函,可以对水进行这样的描述。SCAN 准确地描述了决定液态水结构和动力学的共价键、氢键和范德华相互作用之间的平衡。值得注意的是,SCAN 捕捉到了在环境条件下水和冰 I 之间的密度差,以及液态水的许多重要结构、电子和动力学性质。这种对多功能 SCAN 函数的成功预测为研究水相化学中的复杂过程以及水与其他材料的相互作用打开了大门,使我们能够以高效、准确和可预测的从头算方式进行研究。