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Machine learning for composite materials

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Abstract

Machine learning (ML) has been perceived as a promising tool for the design and discovery of novel materials for a broad range of applications. In this prospective paper, we summarize recent progress in the applications of ML to composite materials modeling and design. An overview of how different types of ML algorithms can be applied to accelerate composite research is presented. This framework is envisioned to revolutionize approaches to design and optimize composites for the next generation of materials with unprecedented properties.

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Acknowledgments

The authors acknowledge support from the Regents of the University of California, Berkeley.

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  1. Department of Materials Science and Engineering, University of California, Berkeley, CA, 94720, USA

    Chun-Teh Chen

  2. Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA

    Grace X. Gu

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Chen, CT., Gu, G.X. Machine learning for composite materials. MRS Communications 9, 556–566 (2019). https://doi.org/10.1557/mrc.2019.32

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