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Title: From ab initio to continuum: Linking multiple scales using deep-learned forces
We develop a deep learning-based algorithm, called DeepForce, to link ab initio physics with the continuum theory to predict concentration profiles of confined water. We show that the deep-learned forces can be used to predict the structural properties of water confined in a nanochannel with quantum scale accuracy by solving the continuum theory given by Nernst–Planck equation. The DeepForce model has an excellent predictive performance with a relative error less than 7.6% not only for confined water in small channel systems (L < 6 nm) but also for confined water in large channel systems (L = 20 nm) which are computationally inaccessible through the high accuracy ab initio molecular dynamics simulations. Finally, we note that classical Molecular dynamics simulations can be inaccurate in capturing the interfacial physics of water in confinement (L < 4.0 nm) when quantum scale physics are neglected.  more » « less
Award ID(s):
2137157
PAR ID:
10498842
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
AIP
Date Published:
Journal Name:
The Journal of Chemical Physics
Volume:
159
Issue:
18
ISSN:
0021-9606
Page Range / eLocation ID:
184108
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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