In predicting polyethylene diffusion using MD-GAN, the unobserved transition of diffusion was successfully predicted, even though the time scale of the training data was limited to the anomalous diffusion region.
Permeation through polymer membranes is an important
technology
in the chemical industry, and in its design, the self-diffusion coefficient
is one of the physical quantities that determine permeability. Since
the self-diffusion coefficient sensitively reflects intra- and intermolecular
interactions, analysis using an all-atom model is required. However,
all-atom simulations are computationally expensive and require long
simulation times for the diffusion of small molecules dissolved in
polymers. MD-GAN, a machine learning model, is effective in accelerating
simulations and reducing computational costs. The target systems for
MD-GAN prediction were limited to polyethylene melts in previous studies;
therefore, this study extended MD-GAN to systems containing copolymers
with branches and successfully predicted water diffusion in various
polymers. The correlation coefficient between the predicted self-diffusion
coefficient and that of the long-time simulation was 1.00. Additionally,
we found that incorporating statistical domain knowledge into MD-GAN
improved accuracy, reducing the mean-square displacement prediction
outliers from 14.6% to 5.3%. Lastly, the distribution of latent variables
with embedded dynamics information within the model was found to be
strongly related to accuracy. We believe that these findings can be
useful for the practical applications of MD-GAN.
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