基于新势能面的石墨烯表面 H2O 吸附动力学

Jun Chen , Tan Jin , Zhe-Ning Chen , Chong Liu , Wei Zhuang
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引用次数: 0

摘要

水与石墨烯之间的相互作用对于理解疏水表面上水的热力学和动力学特性非常重要。在本研究中,我们利用多体展开方案和神经网络拟合构建了水-石墨烯体系的高维势能面(PES)。通过分析 PES 的景观,我们发现水分子表现出弱的物理吸附行为,其结合能约为 - 1000 cm-1,扩散阻力非常低。此外,我们还进行了广泛的分子动力学研究,探讨了单个水在 50 至 300 K 温度范围内对石墨烯表面的吸附和扩散动力学,并根据轨迹计算了平均力势,从而全面准确地描述了水与石墨烯的相互作用动力学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adsorption kinetics of H2O on graphene surface based on a new potential energy surface

The interaction between water and graphene is important for understanding the thermodynamic and kinetic properties of water on hydrophobic surfaces. In this study, we constructed a high-dimensional potential energy surface (PES) for the water-graphene system using the many-body expansion scheme and neural network fitting. By analyzing the landscape of the PES, we found that the water molecule exhibits a weak physisorption behavior with a binding energy of about − 1000 cm−1 and a very low diffusion barrier. Furthermore, extensive molecular dynamics were performed to investigate the adsorption and diffusion dynamics of a single water on a graphene surface at temperatures ranging from 50 to 300 K. Potential-of-mean-forces were computed from the trajectories, providing a comprehensive and accurate description of the water-graphene interaction kinetics.

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Artificial intelligence chemistry
Artificial intelligence chemistry Chemistry (General)
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