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Title: Integrated data-driven modeling and experimental optimization of granular hydrogel matrices
Granular hydrogel matrices have emerged as promising candidates for cell encapsulation, bioprinting, and tissue engineering. How- ever, it remains challenging to design and optimize these materials given their broad compositional and processing parameter space. Here, we combine experimentation and computation to create granular matrices composed of alginate-based bioblocks with controlled structure, rheological properties, and injectability pro- files. A custom machine learning pipeline is applied after each phase of experimentation to automatically map the multidimensional input-output patterns into condensed data-driven models. These models are used to assess generalizable predictability and define high-level design rules to guide subsequent phases of development and characterization. Our integrated, modular approach opens new avenues to understanding and controlling the behavior of complex soft materials.  more » « less
Award ID(s):
2011754
PAR ID:
10501537
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Cell Press
Date Published:
Journal Name:
Matter
Volume:
6
Issue:
3
ISSN:
2590-2385
Page Range / eLocation ID:
1015 to 1036
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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