论文标题
水性fe $^{2+} $/fe $^{3+} $ redox反应的杂种功能和平面波的摘要分子动力学研究
Hybrid Functional and Plane Waves based Ab Initio Molecular Dynamics Study of the Aqueous Fe$^{2+}$/Fe$^{3+}$ Redox Reaction
论文作者
论文摘要
Kohn-Sham密度功能理论和基于平面波集的基于Ab Initio分子动力学（AIMD）模拟是研究溶液中复杂反应的强大工具，例如电子传递（ET）反应，涉及涉及水中水中的Fe $^{2+} $/fe $^{3+} $离子。在大多数情况下，使用在广义梯度近似（GGA）级别上使用密度函数进行此类模拟。建模ET反应的挑战是，由于不可避免的自我交互误差（SIE），GGA功能在预测此类开放式系统的性质中的质量差。尽管混合功能可以最大程度地减少SIE，但对于包含〜100个原子的系统，在该理论水平的AIMD通常比GGA慢150倍。在报告使用混合功能加速AIMD模拟的几种方法中，噪声稳定的MD（NSMD）程序以及使用局部轨道来计算所需的交换积分，是一个有吸引力的选择。在这项工作中，我们演示了NSMD方法在水中研究Fe $^{2+} $/fe $^{3+} $ REDOX反应的应用。这里显示的是，可以使用这种方法获得杂种密度函数水平的长AIMD轨迹。将从这些仿真计算的水性fe $^{2+} $/fe $^{3+} $系统的氧化还原属性与可用的实验数据进行了比较。
Kohn-Sham density functional theory and plane wave basis set based ab initio molecular dynamics (AIMD) simulation is a powerful tool for studying complex reactions in solutions, such as electron transfer (ET) reactions involving Fe$^{2+}$/Fe$^{3+}$ ions in water. In most cases, such simulations are performed using density functionals at the level of Generalized Gradient Approximation (GGA). The challenge in modelling ET reactions is the poor quality of GGA functionals in predicting properties of such open-shell systems due to the inevitable self-interaction error (SIE). While hybrid functionals can minimize SIE, AIMD at that level of theory is typically 150 times slower than GGA for systems containing ~100 atoms. Among several approaches reported to speed-up AIMD simulations with hybrid functionals, the noise-stabilized MD (NSMD) procedure, together with the use of localized orbitals to compute the required exchange integrals, is an attractive option. In this work, we demonstrate the application of the NSMD approach for studying the Fe$^{2+}$/Fe$^{3+}$ redox reaction in water. It is shown here that long AIMD trajectories at the level of hybrid density functionals can be obtained using this approach. Redox properties of the aqueous Fe$^{2+}$/Fe$^{3+}$ system computed from these simulations are compared with the available experimental data for validation.
