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Spatial patterning of active force centers controls folding pathways in elastic networks
Phys. Rev. Materials 10, 045601 – Published 2 April, 2026
DOI: https://doi.org/10.1103/yx3x-1hs9
Abstract
We study the effect of the spatial distribution of active force dipoles on the folding pathways and mechanical stability of rigid-elastic networks using Langevin dynamics simulations. While it has been shown by D. Majumdar, et al. [J. Chem. Phys. 163, 114902 (2025)] that a sharp collapse transition is evident in triangular (elastic) bead-spring networks under the action of contractile (or extensile) force dipoles distributed randomly across the network, here, we show that when the spatial distribution is correlated, e.g., like a patch in the center (“active core” model) or a bandlike distribution along the periphery (“active periphery” model), the network undergoes only a partial decrease in size even at large forces, thereby showing an enhanced mechanical stability just from a spatial rearrangement of the active dipoles. Furthermore, an active periphery network exhibits higher mechanical stability initially, for a range of forces, beyond which the active core network becomes more stable. Active fluctuation induced deformation becomes irreversible beyond a threshold force amplitude, which depends on the type of distribution; for a uniform distribution of active dipoles, the irreversibility threshold almost coincides with the critical collapse point, it decreases for the active core network, and is decreased further for the active periphery network. It is demonstrated that irreversibility arises due to plastic deformations, specifically crease formation, which remains irreversible even after the force is turned off or reversed. The folding pathways depend weakly on the temporal stochasticity of the active links, but are highly sensitive to any defects (missing bonds) in the network. Our findings, therefore, suggest active force localization (or delocalization) as a prime method to dynamically alter the mechanical stability and reversibility of the underlying elastic network.
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