Bayesian Machine Learning in metamaterial design: Fragile becomes supercompressible

Miguel A. Bessa*, Piotr Głowacki, Michael Houlder

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    48 Citations (Scopus)
    194 Downloads (Pure)

    Abstract

    Designing future-proof materials goes beyond a quest for the best. The next generation of materials needs to be adaptive, multipurpose, and tunable. This is not possible by following the traditional experimentally guided trial-and-error process, as this limits the search for untapped regions of the solution space. Here, a computational data-driven approach is followed for exploring a new metamaterial concept and adapting it to different target properties, choice of base materials, length scales, and manufacturing processes. Guided by Bayesian machine learning, two designs are fabricated at different length scales that transform brittle polymers into lightweight, recoverable, and supercompressible metamaterials. The macroscale design is tuned for maximum compressibility, achieving strains beyond 94% and recoverable strengths around 0.1 kPa, while the microscale design reaches recoverable strengths beyond 100 kPa and strains around 80%. The data-driven code is available to facilitate future design and analysis of metamaterials and structures (https://github.com/mabessa/F3DAS).

    Original languageEnglish
    Article number1904845
    Number of pages6
    JournalAdvanced Materials
    Volume31
    Issue number48
    DOIs
    Publication statusPublished - 2019

    Keywords

    • additive manufacturing
    • data-driven design
    • deep learning
    • machine learning
    • optimization

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