Adaptive nonequilibrium design of actin-based metamaterials: Fundamental and practical limits of control

The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials. In particular, controlling the conditions of self-assembly can modulate material properties, but there is no system...

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Published in:Proceedings of the National Academy of Sciences - PNAS Vol. 121; no. 8; p. e2310238121
Main Authors: Chennakesavalu, Shriram, Manikandan, Sreekanth K, Hu, Frank, Rotskoff, Grant M
Format: Journal Article
Language:English
Published: United States National Academy of Sciences 20-02-2024
Proceedings of the National Academy of Sciences
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Summary:The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials. In particular, controlling the conditions of self-assembly can modulate material properties, but there is no systematic understanding of either how to parameterize external control or how controllable a given material can be. Here, we demonstrate that branched actin networks can be encoded with metamaterial properties by dynamically controlling the applied force under which they grow and that the protocols can be selected using multi-task reinforcement learning. These actin networks have tunable responses over a large dynamic range depending on the chosen external protocol, providing a pathway to encoding "memory" within these structures. Interestingly, we obtain a bound that relates the dissipation rate and the rate of "encoding" that gives insight into the constraints on control-both physical and information theoretical. Taken together, these results emphasize the utility and necessity of nonequilibrium control for designing self-assembled nanostructures.
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content type line 23
USDOE
SC0022917
Edited by Monica Olvera de la Cruz, Northwestern University, Evanston, IL; received July 5, 2023; accepted November 13, 2023
ISSN:0027-8424
1091-6490
1091-6490
DOI:10.1073/pnas.2310238121