Phase diagrams of polymer-containing liquid mixtures with a theory-embedded neural network

Authors

Issei Nakamura, Michigan Technological UniversityFollow

Document Type

Article

Publication Date

1-27-2020

Department

Department of Physics

Abstract

We develop a deep neural network (DNN) that accounts for the phase behaviors of polymer-containing liquid mixtures. The key component in the DNN consists of a theory-embedded layer that captures the characteristic features of the phase behavior via coarse-grained mean-field theory and scaling laws and substantially enhances the accuracy of the DNN. Moreover, this layer enables us to reduce the size of the DNN for the phase diagrams of the mixtures. This study also presents the predictive power of the DNN for the phase behaviors of polymer solutions and salt-free and salt-doped diblock copolymer melts.

Publisher's Statement

Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Publisher’s version of record: https://doi.org/10.1088/1367-2630/ab68fc

Publication Title

New Journal of Physics

Recommended Citation

Nakamura, I. (2020). Phase diagrams of polymer-containing liquid mixtures with a theory-embedded neural network. New Journal of Physics, 22. http://doi.org/10.1088/1367-2630/ab68fc
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1703

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 License.

Version

Publisher's PDF