Surrogate molecular dynamics simulation model for dielectric constants with ensemble neural networks
Document Type
Article
Publication Date
10-5-2022
Department
Department of Physics
Abstract
We develop ensemble neural networks (ENN) that serve as computationally fast surrogate models of Stockmayer fluid molecular dynamics (MD) simulations for determining the dielectric constants of polar solvents and NaCl solutions. The ENNs are trained using 50-times less data than is used to calculate the dielectric constants from MD simulations. The predictions of ENNs trained on this small amount of data and using batch normalization or bagging are in relatively good agreement with the full MD results. These ENN methods are thus able to extract reliable values from statistically noisy data.
Publication Title
MRS Communications
Recommended Citation
Gao, T.,
Shock, C. J.,
Stevens, M.,
Frischknecht, A.,
&
Nakamura, I.
(2022).
Surrogate molecular dynamics simulation model for dielectric constants with ensemble neural networks.
MRS Communications,
12(5), 966-974.
http://doi.org/10.1557/s43579-022-00283-5
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16490