AI‐Enabled Materials Design of Non‐Periodic 3D Architectures With Predictable Direction‐Dependent Elastic Properties

Author:

Deng Weiting1,Kumar Siddhant2,Vallone Alberto3,Kochmann Dennis M.3,Greer Julia R.14ORCID

Affiliation:

1. Division of Engineering and Applied Sciences California Institute of Technology Pasadena CA 91125 USA

2. Department of Materials Science and Engineering Delft University of Technology Delft 2628 CD The Netherlands

3. Department of Mechanical and Process Engineering ETH Zurich Zurich 8092 Switzerland

4. Kavli Nanoscience Institute at Caltech Pasadena CA 91125 USA

Abstract

AbstractNatural porous materials have exceptional properties—for example, light weight, mechanical resilience, and multi‐functionality. Efforts to imitate their properties in engineered structures have limited success. This, in part, is caused by the complexity of multi‐phase materials composites and by the lack of quantified understanding of each component's role in overall hierarchy. This challenge is twofold: 1) computational. because non‐periodicity and defects render constructing design guidelines between geometries and mechanical properties complex and expensive and 2) experimental. because the fabrication and characterization of complex, often hierarchical and non‐periodic 3D architectures is non‐trivial.

Funder

Office of Naval Research Global

Publisher

Wiley

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