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Title: Transferability evaluation of the deep potential model for simulating water-graphene confined system
Machine learning potentials (MLPs) are poised to combine the accuracy of ab initio predictions with the computational efficiency of classical molecular dynamics (MD) simulation. While great progress has been made over the last two decades in developing MLPs, there is still much to be done to evaluate their model transferability and facilitate their development. In this work, we construct two deep potential (DP) models for liquid water near graphene surfaces, Model S and Model F, with the latter having more training data. A concurrent learning algorithm (DP-GEN) is adopted to explore the configurational space beyond the scope of conventional ab initio MD simulation. By examining the performance of Model S, we find that an accurate prediction of atomic force does not imply an accurate prediction of system energy. The deviation from the relative atomic force alone is insufficient to assess the accuracy of the DP models. Based on the performance of Model F, we propose that the relative magnitude of the model deviation and the corresponding root-mean-square error of the original test dataset, including energy and atomic force, can serve as an indicator for evaluating the accuracy of the model prediction for a given structure, which is particularly applicable for large systems where density functional theory calculations are infeasible. In addition to the prediction accuracy of the model described above, we also briefly discuss simulation stability and its relationship to the former. Both are important aspects in assessing the transferability of the MLP model.  more » « less
Award ID(s):
1940118
PAR ID:
10467933
Author(s) / Creator(s):
; ;
Publisher / Repository:
API Water
Date Published:
Journal Name:
The Journal of Chemical Physics
Volume:
159
Issue:
4
ISSN:
0021-9606
Page Range / eLocation ID:
Article number 044712
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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