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Fast inverse design of microstructures via generative invariance networks

Nature Computational Science volume 1, pages 229–238 (2021)Cite this article

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Abstract

The problem of the efficient design of material microstructures exhibiting desired properties spans a variety of engineering and science applications. The ability to rapidly generate microstructures that exhibit user-specified property distributions can transform the iterative process of traditional microstructure-sensitive design. We reformulate the microstructure design process using a constrained generative adversarial network (GAN) model. This approach explicitly encodes invariance constraints within GANs to generate two-phase morphologies for photovoltaic applications obeying design specifications: specifically, user-defined short-circuit current density and fill factor combinations. Such invariance constraints can be represented by differentiable, deep learning-based surrogates of full physics models mapping microstructures to photovoltaic properties. Furthermore, we propose a multi-fidelity surrogate that reduces expensive label requirements by a factor of five. Our framework enables the incorporation of expensive or non-differentiable constraints for the fast generation of microstructures (in 190 ms) with user-defined properties. Such proposed physics-aware data-driven methods for inverse design problems can be used to considerably accelerate the field of microstructure-sensitive design.

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Fig. 1: Overview of the InvNet framework for targeted microstructure generation based on user design specifications.
Fig. 2: Results of the high-fidelity and multi-fidelity surrogate models.
Fig. 3: Results of targeted microstructure design using multi-fidelity InvNet.
Fig. 4: Qualitative comparison of morphologies generated by the high-fidelity InvNet versus the multi-fidelity InvNet.

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Data availability

The data generation procedure for the dataset used in this study is explained in the Methods of the manuscript, and the generated dataset is available from the repository in ref. 44.

Code availability

The codes for generating the dataset is available in the following code repository45. The data and code used to train the models are available in a code capsule46 and the code used to generate the figures and results of this study is available in a separate code capsule47. Source data are provided with this paper.

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Acknowledgements

This work was supported by the ARPA-E DIFFERENTIATE programme under grant no. DE-AR0001215. B.G., C.-H.Y. and B.S.S.P. were supported in part by DoD MURI 6119-ISU-ONR-2453. C.H. and A.J. were supported in part by NSF grants CCF-2005804 and CCF-1815101. We thank A. Krishnamurthy and Z. Bao for fruitful discussions and constructive suggestions. Computing support from XSEDE and Iowa State University is also gratefully acknowledged.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Iowa State University, Ames, IA, USA

    Xian Yeow Lee, Joshua R. Waite, Chih-Hsuan Yang, Balaji Sesha Sarath Pokuri, Aditya Balu, Baskar Ganapathysubramanian & Soumik Sarkar

  2. Tandon School of Engineering, New York University, Brooklyn, NY, USA

    Ameya Joshi & Chinmay Hegde

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Contributions

C.H., B.G. and S.S. initiated the project; B.S.S.P. and B.G. planned and generated the dataset. X.Y.L., A.J., C.H., B.G. and S.S. designed the machine learning framework. X.Y.L., J.R.W. and C.-H.Y. performed the training. X.Y.L., A.J. and A.B. analyzed the data. All authors contributed to writing the manuscript.

Corresponding authors

Correspondence to Baskar Ganapathysubramanian or Soumik Sarkar.

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The authors declare no competing interests.

Additional information

Peer review information Nature Computational Science thanks Samuel Cooper and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Jie Pan was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Information, Discussion, Figs. 1–9 and Tables 1–7.

Source data

Source Data Fig. 2

Statistical source data to generate correlation and error density plots.

Source Data Fig. 3

Image of generated microstructures and statistical source data to generate density plots.

Source Data Fig. 4

Image of generated microstructures and statistical source data to generate density plots.

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Lee, X.Y., Waite, J.R., Yang, CH. et al. Fast inverse design of microstructures via generative invariance networks. Nat Comput Sci 1, 229–238 (2021). https://doi.org/10.1038/s43588-021-00045-8

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