Microstructural materials design via deep adversarial learning methodology

Zijiang Yang, Xiaolin Li, L Catherine Brinson, Alok N. Choudhary, Wei Chen, Ankit Agrawal

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

Identifying the key microstructure representations is crucial for computational materials design (CMD). However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for microstructural materials design. Some MCR approaches are not applicable for microstructural materials design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for microstructural materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural materials design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration.

Original languageEnglish (US)
Article number111416
JournalJournal of Mechanical Design, Transactions of the ASME
Volume140
Issue number11
DOIs
StatePublished - Nov 2018

Keywords

  • Bayesian optimization
  • Deep learning
  • Generative adversarial network
  • Microstructural analysis
  • Microstructural materials design
  • Scalability
  • Transfer learning

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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