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arXiv 2006.01929physics.chem-ph
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氢键的最小实验偏差极大地改进了水的从头计算分子动力学模拟

A Minimal Experimental Bias on the Hydrogen Bond Greatly Improves Ab Initio Molecular Dynamics Simulations of Water

Paul B. Calio, Glen M. Hocky, Gregory A. Voth

摘要:

实验指导模拟(EDS)是一类技术中的一种方法,旨在通过最小限度地偏置系统哈密顿量来重现某些实验观测值来改善分子模拟。在以前的应用EDS从头算分子动力学(AIMD)模拟的基础上,电子密度泛函理论(DFT),AIMD模拟水的偏向再现其实验衍生的溶剂化结构。特别地,通过单独偏置O-O对关联函数,未偏置的其他结构和动力学性质得到改善。在这项工作中,假设进行了测试,直接偏置OH对相关性,将提供一个更好的改善DFT为基础的水属性在AIMD模拟。这一假设背后的逻辑是,对于大多数水的电子DFT描述,由于DFT中的异常电荷转移和过度极化,氢键是已知不足的。使用EDS学习算法的最新进展,我们因此对AIMD水进行了最小偏差的训练,该偏差再现了从水的高度精确的MB-pol模型导出的O-H径向分布函数。然后证实,单独偏置O-H对相关性可以改善AIMD的水性质,结构和动力学性质比以前的EDS-AIMD模型更接近实验。

英文摘要:

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.

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