Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, United States.
HEDPS, Center for Applied Physics and Technology, College of Engineering, Peking University, Beijing 100871, China.
J Phys Chem B. 2021 Oct 21;125(41):11444-11456. doi: 10.1021/acs.jpcb.1c03884. Epub 2021 Sep 17.
Within the framework of Kohn-Sham density functional theory (DFT), the ability to provide good predictions of water properties by employing a strongly constrained and appropriately normed (SCAN) functional has been extensively demonstrated in recent years. Here, we further advance the modeling of water by building a more accurate model on the fourth rung of Jacob's ladder with the hybrid functional, SCAN0. In particular, we carry out both classical and Feynman path-integral molecular dynamics calculations of water with the SCAN0 functional and the isobaric-isothermal ensemble. To generate the equilibrated structure of water, a deep neural network potential is trained from the atomic potential energy surface based on data obtained from SCAN0 DFT calculations. For the electronic properties of water, a separate deep neural network potential is trained by using the Deep Wannier method based on the maximally localized Wannier functions of the equilibrated trajectory at the SCAN0 level. The structural, dynamic, and electric properties of water were analyzed. The hydrogen-bond structures, density, infrared spectra, diffusion coefficients, and dielectric constants of water, in the electronic ground state, are computed by using a large simulation box and long simulation time. For the properties involving electronic excitations, we apply the GW approximation within many-body perturbation theory to calculate the quasiparticle density of states and bandgap of water. Compared to the SCAN functional, mixing exact exchange mitigates the self-interaction error in the meta-generalized-gradient approximation and further softens liquid water toward the experimental direction. For most of the water properties, the SCAN0 functional shows a systematic improvement over the SCAN functional. However, some important discrepancies remain. The H-bond network predicted by the SCAN0 functional is still slightly overstructured compared to the experimental results.
在 Kohn-Sham 密度泛函理论（DFT）的框架内，近年来，通过使用强约束和适当归一化（SCAN）泛函，已经广泛证明了该理论能够很好地预测水的性质。在这里，我们通过使用混合泛函 SCAN0 在 Jacob 梯级的第四级上构建更准确的模型，进一步推进水的建模。特别是，我们使用 SCAN0 泛函和等压等温热力学系综进行了水的经典和费曼路径积分分子动力学计算。为了生成水的平衡结构，从基于 SCAN0 DFT 计算获得的数据的原子势能表面训练了一个深度神经网络势。对于水的电子性质，使用基于平衡轨迹的最大局域化 Wannier 函数的 Deep Wannier 方法训练了一个单独的深度神经网络势。分析了水的结构、动态和电性质。使用大模拟盒和长模拟时间计算了电子基态下水的氢键结构、密度、红外光谱、扩散系数和介电常数。对于涉及电子激发的性质，我们应用多体微扰理论中的 GW 近似来计算水的准粒子态密度和能隙。与 SCAN 泛函相比，混合精确交换缓解了泛函在meta-GGA 中的自相互作用误差，并进一步将液态水向实验方向软化。对于大多数水的性质，SCAN0 泛函显示出比 SCAN 泛函系统的改善。然而，一些重要的差异仍然存在。与实验结果相比，SCAN0 泛函预测的氢键网络仍然略微过结构化。
