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

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