Reptation-guided machine learning to characterize ultrahigh molecular weight polyethylene gels
발표자
()
초록
내용
We have suggested the analogy between the double reptation and the deep neural network model. The double reptation theory itself can be the special case of the deep learning method; the linear activation function and the identical weights for the two-layers are the characteristics of the double reptation model. The identical weights in the double reptation model are related with the molecular weight distribution. We showed that the deep neural network model is available to determine, the molecular weight distribution, entanglement molecular weight (plateau modulus), and monomeric friction factors from the experimental rheological data without any additional information using properly trained models. Overall, a noteworthy conceptual improvement in the determination of major factors that determine the rheological behavior has been achieved.