Application of deep learning to inverse design of phase separation structure in polymer alloy

Kazuya Hiraide, Kenta Hirayama, Katsuhiro Endo, Mayu Muramatsu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this study, using some machine learning methods, we develop a framework that deals with forward analysis to predict a property from a polymer alloy's phase separation structure and inverse design to generate the structure from the property. We only consider Young's modulus as the property in this study. The forward analysis is performed using a convolutional neural network (CNN) and the inverse design is realized by a random search toward a model combining a generative adversarial network (GAN) and a CNN. This framework is applicable to other properties at a low computational cost, and latent variables belonging to the GAN are useful for feature extraction.

Original languageEnglish
Article number110278
JournalComputational Materials Science
Volume190
DOIs
Publication statusPublished - 2021 Apr 1

Keywords

  • Deep learning
  • Inverse design
  • Phase separation structure
  • Polymer alloy

ASJC Scopus subject areas

  • Computer Science(all)
  • Chemistry(all)
  • Materials Science(all)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Computational Mathematics

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