Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural Operators Article Swipe
Hanxun Jin
,
Boyu Zhang
,
Qianying Cao
,
Enrui Zhang
,
Aniruddha Bora
,
Sridhar Krishnaswamy
,
George Em Karniadakis
,
Horacio D. Espinosa
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/adma.202420063
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/adma.202420063
Machine learning (ML) is emerging as a transformative tool for the design of mechanical metamaterials, offering properties that far surpass those achievable through lab‐based trial‐and‐error methods. However, a major challenge in current inverse design strategies is their reliance on extensive computational and/or experimental datasets, which becomes particularly problematic for designing micro‐scale stochastic architected materials that exhibit nonlinear mechanical behaviors. Here, a comprehensive end‐to‐end scientific ML framework, leveraging deep neural operators (including DeepONet and its variants) is introduced, to directly learn the relationship between the complete microstructure and mechanical response of architected metamaterials from sparse but high‐quality in situ experimental data. Various neural operators and standard neural networks are systematically compared to identify the model that offers better interpretability and accuracy. The approach facilitates the efficient inverse design of structures tailored to specific nonlinear mechanical behaviors. Results obtained from stochastic spinodal microstructures, printed using two‐photon lithography, reveal that the prediction error for mechanical responses is within a range of 5 ‐ 10%. This work underscores that by employing neural operators with advanced nano‐ and micro‐mechanical experiments, the design of complex micro‐architected materials with desired properties becomes feasible, even in scenarios constrained by data scarcity. This work marks a significant advancement in the field of materials‐by‐design, potentially heralding a new era in the discovery and development of next‐generation metamaterials with unparalleled mechanical characteristics derived directly from experimental insights.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/adma.202420063
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/adma.202420063
- OA Status
- hybrid
- Cited By
- 9
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409649139
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409649139Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/adma.202420063Digital Object Identifier
- Title
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Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural OperatorsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-21Full publication date if available
- Authors
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Hanxun Jin, Boyu Zhang, Qianying Cao, Enrui Zhang, Aniruddha Bora, Sridhar Krishnaswamy, George Em Karniadakis, Horacio D. EspinosaList of authors in order
- Landing page
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https://doi.org/10.1002/adma.202420063Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/adma.202420063Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/adma.202420063Direct OA link when available
- Concepts
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Metamaterial, Interpretability, Artificial neural network, Inverse, Materials science, Computer science, Nonlinear system, Artificial intelligence, Machine learning, Mechanical engineering, Physics, Engineering, Mathematics, Quantum mechanics, Optoelectronics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
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2025: 9Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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