Artificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants Article Swipe
Pengcheng Jiao
,
Chenjie Zhang
,
Wenxuan Meng
,
Jiajun Wang
,
Daeik Jang
,
Zhangming Wu
,
Nitin Agarwal
,
Amir H. Alavi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acsami.4c17625
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acsami.4c17625
Current limitations in implant design often lead to trade-offs between minimally invasive surgery and achieving the desired post-implantation functionality. Here, we present an artificial intelligence inverse design paradigm for creating deployable implants as planar and tubular thermal mechanical metamaterials (thermo-metamaterials). These thermo-metamaterial implants exhibit tunable mechanical properties and volume change in response to temperature changes, enabling minimally invasive and personalized surgery. We begin by generating a large database of corrugated thermo-metamaterials with various cell structures and bending stiffnesses. An artificial intelligence inverse design model is subsequently developed by integrating an evolutionary algorithm with a neural network. This model allows for the automatic determination of the optimal microstructure for thermo-metamaterials with desired performance,i.e., target bending stiffness. We validate this approach by designing patient-specific spinal fusion implants and tracheal stents. The results demonstrate that the deployable thermo-metamaterial implants can achieve over a 200% increase in volume or cross-sectional area in their fully deployed states. Finally, we propose a broader vision for a clinically informed artificial intelligence design process that prioritizes biocompatibility, feasibility, and precision simultaneously for the development of high-performing and clinically viable implants. The feasibility of this proposed vision is demonstrated using a fuzzy analytic hierarchy process to customize thermo-metamaterial implants based on clinically relevant factors.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acsami.4c17625
- OA Status
- hybrid
- Cited By
- 10
- References
- 39
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406014077Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1021/acsami.4c17625Digital Object Identifier
- Title
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Artificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial ImplantsWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-02Full publication date if available
- Authors
-
Pengcheng Jiao, Chenjie Zhang, Wenxuan Meng, Jiajun Wang, Daeik Jang, Zhangming Wu, Nitin Agarwal, Amir H. AlaviList of authors in order
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https://doi.org/10.1021/acsami.4c17625Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1021/acsami.4c17625Direct OA link when available
- Concepts
-
Metamaterial, Materials science, Computer science, Inverse, Process (computing), Artificial intelligence, Nanotechnology, Optoelectronics, Operating system, Geometry, MathematicsTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 10Per-year citation counts (last 5 years)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.biocompatibility, | 168 |
| abstract_inverted_index.performance,i.e., | 111 |
| abstract_inverted_index.post-implantation | 17 |
| abstract_inverted_index.thermo-metamaterial | 41, 134, 198 |
| abstract_inverted_index.thermo-metamaterials | 70, 108 |
| abstract_inverted_index.(thermo-metamaterials). | 39 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5114198520, https://openalex.org/A5081817597 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I170201317, https://openalex.org/I31847773, https://openalex.org/I76130692 |
| citation_normalized_percentile.value | 0.98728251 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |