A Deep Neural Network Potential for Water Confined in Graphene Nanocapillaries,The Journal of Physical Chemistry C

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A Deep Neural Network Potential for Water Confined in Graphene Nanocapillaries
The Journal of Physical Chemistry C ( IF 3.3 ) Pub Date : 2022-06-16 , DOI: 10.1021/acs.jpcc.2c02423
Wen Zhao ₁ , Hu Qiu ₁ , Wanlin Guo ₁

Affiliation

Water under nanoconfinement exhibits structural and dynamical properties remarkably different from those of bulk water, and empirical potential-based molecular dynamics has played an important role in furthering our understanding of the behavior of confined water. However, existing potentials for water were commonly parametrized based on the properties of the bulk water and may be unreliable for describing nanoconfined water. Here, we develop a machine learning potential for water confined in graphene nanocapillaries using deep neural networks trained on quantum mechanical density-functional theory (DFT) calculations. This deep neural network potential offers near-DFT accuracy in terms of potential energy and atomic forces but at a computational cost much lower than DFT-based ab initio molecular dynamics methods. In particular, this potential reproduces well the DFT reference for a wide range of properties, including O–H bond length distribution, density distribution, radial distribution functions, hydrogen bonding, etc. The developed deep neural network potential opens the door to simulations of nanoconfined water with large system sizes and time scales at near-DFT accuracy.

中文翻译：

石墨烯纳米毛细管中水的深度神经网络潜力

纳米约束下的水表现出与散装水显着不同的结构和动力学特性，基于经验势的分子动力学在加深我们对承压水行为的理解方面发挥了重要作用。然而，现有的水势通常是根据散装水的性质参数化的，并且对于描述纳米约束水可能不可靠。在这里，我们使用经过量子力学密度泛函理论 (DFT) 计算训练的深度神经网络，开发了一种机器学习潜力，用于限制石墨烯纳米毛细管中的水。这种深度神经网络的潜力在势能和原子力方面提供了接近 DFT 的精度，但计算成本远低于基于 DFT 的从头算分子动力学方法。特别是，该电位很好地再现了 DFT 参考的广泛特性，包括 O-H 键长分布、密度分布、径向分布函数、氢键等。开发的深度神经网络潜力为以近 DFT 精度模拟具有大系统尺寸和时间尺度的纳米承压水打开了大门。

更新日期：2022-06-16

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