论文标题
对氢键的最小实验偏差极大地改善了从头开始的分子动力学模拟。
A Minimal Experimental Bias on the Hydrogen Bond Greatly Improves Ab Initio Molecular Dynamics Simulations of Water
论文作者
论文摘要
实验定向模拟（EDS）是一种试图改善分子模拟的技术中的一种方法，它通过最小化偏置哈密顿量的系统来重现某些实验性可观察物。在先前基于电子密度功能理论（DFT）的IBL分子动力学（AIMD）仿真的先前应用中，对水的AIMD模拟有偏见以重现其实验得出的溶剂化结构。特别是，通过仅偏向O-O对相关函数，改善了其他没有偏置的结构和动力学特性。在这项工作中，检验了该假设，即直接偏向OH对相关性，将在AIMD模拟中更好地改善基于DFT的水性质。该假设背后的逻辑是，对于大多数电子DFT描述了水，氢键因异常电荷转移而缺乏不足，并且在DFT中的极化。因此，利用EDS学习算法的最新进展，我们在AIMD水上训练最小的偏差，该偏差又繁殖了从高度准确的水的MB-POL模型中得出的O-H径向分布函数。然后证实，仅偏置O-H对相关性就可以改善AIMD水性能，其结构和动力学性能在接近实验的过程中比以前的EDS-AIMD模型更接近实验。
Experiment Directed Simulations (EDS) is a method within a class of techniques seeking to improve molecular simulations by minimally biasing the system Hamiltonian to reproduce certain experimental observables. In a previous application of EDS to ab initio molecular dynamics (AIMD) simulation-based on electronic density functional theory (DFT), the AIMD simulations of water were biased to reproduce its experimentally derived solvation structure. In particular, by solely biasing the O-O pair correlation functions, other structural and dynamical properties that were not biased were improved. In this work, the hypothesis is tested that directly biasing the OH pair correlation, will provide an even better improvement of DFT-based water properties in AIMD simulations. The logic behind this hypothesis is that for most electronic DFT descriptions of water the hydrogen bonding is known to be deficient due to anomalous charge transfer and over polarization in the DFT. Using recent advances to the EDS learning algorithm, we thus train a minimal bias on AIMD water that reproduces the O-H radial distribution function derived from the highly accurate MB-pol model of water. It is then confirmed that biasing the O-H pair correlation alone can lead to improved AIMD water properties, with structural and dynamical properties in even closer to experiment than the previous EDS-AIMD model.
