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Title: Efficiently Trained Deep Learning Potential for Graphane
We have developed an accurate and efficient deep-learning potential (DP) for graphane, which is a fully hydrogenated version of graphene, using a very small training set consisting of 1000 snapshots from a 0.5 ps density functional theory (DFT) molecular dynamics simulation at 1000 K. We have assessed the ability of the DP to extrapolate to system sizes, temperatures, and lattice strains not included in the training set. The DP performs surprisingly well, outperforming an empirical many-body potential when compared with DFT data for the phonon density of states, thermodynamic properties, velocity autocorrelation function, and stress–strain curve up to the yield point. This indicates that our DP can reliably extrapolate beyond the limit of the training data. We have computed the thermal fluctuations as a function of system size for graphane. We found that graphane has larger thermal fluctuations compared with graphene, but having about the same out-of-plane stiffness.  more » « less
Award ID(s):
1703266
NSF-PAR ID:
10275421
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
The Journal of Physical Chemistry C
ISSN:
1932-7447
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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