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

Wen Jing Deng, Siddhant Kumar, Alberto Vallone, Dennis M. Kochmann, Julia R. Greer*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Natural 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.

Original languageEnglish
JournalAdvanced Materials
DOIs
Publication statusAccepted/In press - 2024

Funding

The authors gratefully acknowledge financial support from the Office of Naval Research Award N00014‐22‐1‐2384.

Keywords

  • additive manufacturing
  • anisotropy
  • biomimetic
  • machine learning
  • scaffold design

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