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Inverse-designed spinodoid metamaterials

Kumar, Siddhant and Tan, Stephanie and Zheng, Li and Kochmann, Dennis M. (2020) Inverse-designed spinodoid metamaterials. npj Computational Materials, 6 (1). Art. No. 73. ISSN 2057-3960. https://resolver.caltech.edu/CaltechAUTHORS:20200702-081435421

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Abstract

After a decade of periodic truss-, plate-, and shell-based architectures having dominated the design of metamaterials, we introduce the non-periodic class of spinodoid topologies. Inspired by natural self-assembly processes, spinodoid metamaterials are a close approximation of microstructures observed during spinodal phase separation. Their theoretical parametrization is so intriguingly simple that one can bypass costly phase-field simulations and obtain a rich and seamlessly tunable property space. Counter-intuitively, breaking with the periodicity of classical metamaterials is the enabling factor to the large property space and the ability to introduce seamless functional grading. We introduce an efficient and robust machine learning technique for the inverse design of (meta-)materials which, when applied to spinodoid topologies, enables us to generate uniform and functionally graded cellular mechanical metamaterials with tailored direction-dependent (anisotropic) stiffness and density. We specifically present biomimetic artificial bone architectures that not only reproduce the properties of trabecular bone accurately but also even geometrically resemble natural bone.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41524-020-0341-6DOIArticle
ORCID:
AuthorORCID
Kumar, Siddhant0000-0003-1602-8641
Zheng, Li0000-0002-2488-3237
Kochmann, Dennis M.0000-0002-9112-6615
Additional Information:© 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Published in partnership with the Shanghai Institute of Ceramics of the Chinese Academy of Sciences. Received 03 February 2020; Accepted 08 May 2020; Published 05 June 2020. Data availability: The datasets generated during the current study are available from the corresponding author upon reasonable request. Code availability: The machine learning codes used during the current study are available from the corresponding author upon reasonable request. Author Contributions: S.K. and D.M.K. conceived the research. S.K. developed the anisotropic spinodoid topology theory. S.K. and S.T. conceptualized and developed the inverse design framework. L.Z. performed training and optimization of the neural networks. S.K. and D.M.K. wrote the paper. The authors declare no competing interests.
Group:GALCIT
Subject Keywords:Bioinspired materials; Computational methods; Mechanical properties
Issue or Number:1
Record Number:CaltechAUTHORS:20200702-081435421
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200702-081435421
Official Citation:Kumar, S., Tan, S., Zheng, L. et al. Inverse-designed spinodoid metamaterials. npj Comput Mater 6, 73 (2020). https://doi.org/10.1038/s41524-020-0341-6
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104201
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:02 Jul 2020 16:13
Last Modified:02 Jul 2020 16:13

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