Abstract Recent advances in computational design and 3D printing enable the fabrication of polymer lattices with high strength‐to‐weight ratio and tailored mechanics. To date, 3D lattices composed of monolithic materials have primarily been constructed due to limitations associated with most commercial 3D printing platforms. Here, freeform fabrication of multi‐material polymer lattices via embedded three‐dimensional (EMB3D) printing is demonstrated. An algorithm is developed first that generates print paths for each target lattice based on graph theory. The effects of ink rheology on filamentary printing and the effects of the print path on resultant mechanical properties are then investigated. By co‐printing multiple materials with different mechanical properties, a broad range of periodic and stochastic lattices with tailored mechanical responses can be realized opening new avenues for constructing architected matter.
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Polymer-Unit Graph: Advancing Interpretability in Graph Neural Network Machine Learning for Organic Polymer Semiconductor Materials
- Award ID(s):
- 2311117
- PAR ID:
- 10518359
- Publisher / Repository:
- JOURNAL OF CHEMICAL THEORY AND COMPUTATION
- Date Published:
- Journal Name:
- Journal of Chemical Theory and Computation
- Volume:
- 20
- Issue:
- 7
- ISSN:
- 1549-9618
- Page Range / eLocation ID:
- 2908 to 2920
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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