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使用无监督机器学习进行降维对一氧化碳 - 水界面进行从头算表征。

Ab Initio Characterization of the CO-Water Interface Using Unsupervised Machine Learning for Dimensionality Reduction.

作者信息

Morishita Tetsuya, Shiga Masashige

机构信息

Research Center for Computational Design of Advanced Functional Materials (CD-FMat), National Institute of Advanced Industrial Science and Technology (AIST), Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan.

Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST), Central 7, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8567, Japan.

出版信息

J Phys Chem B. 2024 Jun 13;128(23):5781-5791. doi: 10.1021/acs.jpcb.4c01526. Epub 2024 Jun 3.

DOI:10.1021/acs.jpcb.4c01526
PMID:38829554
Abstract

Precise characterization of the supercritical CO-water interface under high pressure and temperature conditions is crucial for the geological storage of carbon dioxide (CO) in deep saline aquifers. Molecular dynamics (MD) simulations offer a valuable approach to gaining insight into the CO-water interface at a microscopic level. However, no attempt has been made to characterize the CO-water interface with the accuracy afforded by calculations. In this study, we performed MD (AIMD) simulations to investigate the structural and dynamical properties of the CO-water interface, comparing the results with those obtained from classical force-field MD (FF-MD) simulations. Molecular orientation at the interface was well reproduced in both AIMD and FF-MD simulations. Characteristic structural fluctuations of water at the interface were unveiled by applying multidimensional scaling and time-dependent principal component analysis to the AIMD trajectories; however, they were not prominent in the FF-MD simulations. Furthermore, our study demonstrated a marked difference in the residence time of molecules in the interface region between AIMD and FF-MD simulations, indicating that time-dependent properties of the CO-water interface strongly depend on the description of the intermolecular forces.

摘要

在高压和高温条件下精确表征超临界CO₂-水界面对于二氧化碳(CO₂)在深部盐水层中的地质储存至关重要。分子动力学(MD)模拟为在微观层面深入了解CO₂-水界面提供了一种有价值的方法。然而,尚未有人尝试以计算所提供的精度来表征CO₂-水界面。在本研究中,我们进行了从头算分子动力学(AIMD)模拟,以研究CO₂-水界面的结构和动力学性质,并将结果与经典力场分子动力学(FF-MD)模拟的结果进行比较。在AIMD和FF-MD模拟中,界面处的分子取向均得到了很好的再现。通过对AIMD轨迹应用多维标度和随时间变化的主成分分析,揭示了界面处水的特征结构波动;然而,它们在FF-MD模拟中并不明显。此外,我们的研究表明,AIMD和FF-MD模拟在界面区域分子的停留时间上存在显著差异,这表明CO₂-水界面的时间相关性质强烈依赖于分子间力的描述。

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