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Title: Deep learning-based quasi-continuum theory for structure of confined fluids
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.  more » « less
Award ID(s):
2140225 2137157
NSF-PAR ID:
10389035
Author(s) / Creator(s):
;
Date Published:
Journal Name:
The Journal of Chemical Physics
Volume:
157
Issue:
8
ISSN:
0021-9606
Page Range / eLocation ID:
084121
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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