Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA.
J Chem Phys. 2022 Aug 28;157(8):084121. doi: 10.1063/5.0096481.
Predicting the structural properties of water and simple fluids confined in nanometer scale pores and channels is essential in, for example, energy storage and biomolecular systems. Classical continuum theories fail to accurately capture the interfacial structure of fluids. In this work, we develop a deep learning-based quasi-continuum theory (DL-QT) to predict the concentration and potential profiles of a Lennard-Jones (LJ) fluid and water confined in a nanochannel. The deep learning model is built based on a convolutional encoder-decoder network (CED) and is applied for high-dimensional surrogate modeling to relate the fluid properties to the fluid-fluid potential. The CED model is then combined with the interatomic potential-based continuum theory to determine the concentration profiles of a confined LJ fluid and confined water. We show that the DL-QT model exhibits robust predictive performance for a confined LJ fluid under various thermodynamic states and for water confined in a nanochannel of different widths. The DL-QT model seamlessly connects molecular physics at the nanoscale with continuum theory by using a deep learning model.
预测水和简单流体在纳米尺度孔和通道中的结构性质对于储能和生物分子系统等领域至关重要。经典的连续体理论无法准确捕捉流体的界面结构。在这项工作中，我们开发了一种基于深度学习的拟连续体理论（DL-QT），以预测受限在纳米通道中的 Lennard-Jones（LJ）流体和水的浓度和势能分布。深度学习模型基于卷积编码器-解码器网络（CED）构建，并应用于高维代理建模，以将流体性质与流体-流体势能相关联。然后，CED 模型与基于原子间势能的连续体理论相结合，以确定受限 LJ 流体和受限水的浓度分布。我们表明，DL-QT 模型在各种热力学状态下对受限 LJ 流体以及不同宽度的纳米通道中的受限水表现出稳健的预测性能。DL-QT 模型通过使用深度学习模型，将纳米尺度的分子物理与连续体理论无缝连接。
