Effect of directed aging on nonlinear elasticity and memory formation in a material

Hexner, Daniel; Pashine, Nidhi; Liu, Andrea J.; Nagel, Sidney R.

doi:10.1103/physrevresearch.2.043231

Cited by 32 publications

(25 citation statements)

References 48 publications

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“…Here, the logarithmic increase of G and G with the strain accumulated under low enough shear rate is strikingly reminiscent of aging and constitutes a clear instance of "overaging" in a colloidal gel. Finally, we note that for vanishingly low shear rates, our results may be also linked to the so-called "directed aging" recently achieved under a static load or strain in amorphous solids [42,43].…”

supporting

confidence: 68%

Residual stresses and shear-induced overaging in boehmite gels

Sudreau¹,

Auxois²,

Servel³

et al. 2022

Preprint

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Colloidal gels respond like soft solids at rest, whereas they flow like liquids under external shear. Starting from a fluidized state under an applied shear rate γp, abrupt flow cessation triggers a liquidto-solid transition during which the stress relaxes towards a so-called residual stress σres that tallies a macroscopic signature of previous shear history. Here, we report on the liquid-to-solid transition in gels of boehmite, an aluminum oxide, that shows a remarkable non-monotonic stress relaxation towards a residual stress σres( γp) characterized by a dual behavior relative to a critical value γc of the shear rate γp. Following shear at γp > γc, the gel obtained upon flow cessation is insensitive to shear history, and the residual stress is negligible. However, for γp < γc, the gel encodes some memory of the shear history, and σres increases for decreasing shear rate, directly contributing to reinforcing the gel viscoelastic properties. Moreover, we show that both σres and the gel viscoelastic properties increase logarithmically with the strain accumulated during the shear period preceding flow cessation. Such a shear-induced "overaging" phenomenon bears great potential for tuning the rheological properties of colloidal gels.

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supporting

confidence: 68%

Residual stresses and shear-induced overaging in boehmite gels

Sudreau¹,

Auxois²,

Servel³

et al. 2022

Preprint

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“…by spring-dashpot networks similar to those studied here (26). Manipulating the Poisson's ratio by pruning bonds, originally conceived in simulations of spring networks (44), has also been successful in experiments on patterned rubber sheets when simulations account for the angular stiffness at the nodes (45).…”

Section: Physicsmentioning

confidence: 78%

“…2B shows that, for a large enough number of cycles, we reach ν ≈ −1 over the range of measuring strains of − Age + Age , even for small Age . Aging at a fixed strain of Age , however, only decreases the Poisson's ratio under compression (26), and by a far smaller amount. At a fixed aging isotropic expansion, ν actually becomes more positive.…”

Section: Training Global Responsementioning

confidence: 93%

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Periodic training of creeping solids

Hexner

Liu

Nagel

2020

Proc. Natl. Acad. Sci. U.S.A.

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We consider disordered solids in which the microscopic elements can deform plastically in response to stresses on them. We show that by driving the system periodically, this plasticity can be exploited to train in desired elastic properties, both in the global moduli and in local “allosteric” interactions. Periodic driving can couple an applied “source” strain to a “target” strain over a path in the energy landscape. This coupling allows control of the system’s response, even at large strains well into the nonlinear regime, where it can be difficult to achieve control simply by design.

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“…In order for such physical networks to learn on their own, they cannot minimize an arbitrary cost function by gradient descent since that is a global process that requires knowing all the microscopic details at once, carrying out the global computation of gradient descent, and then manipulating networks at the microscopic (node or edge) level. Rather, approaches such as contrastive learning (1)(2)(3)(4), equilibrium propagation (5,6), directed aging (7)(8)(9)(10)(11)(12) and coupled learning (13,14) use local rules, in which learning degrees of freedom (e.g. the conductances of edges in electrical networks of variable resistors) respond to physical degrees of freedom (e.g.…”

Section: Introductionmentioning

confidence: 99%

Physical learning beyond the quasistatic limit

Stern,

Dillavou,

Miskin

et al. 2021

Preprint

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Physical networks, such as biological neural networks, can learn desired functions without a central processor, using local learning rules in space and time to learn in a fully distributed manner. Learning approaches such as equilibrium propagation, directed aging, and coupled learning similarly exploit local rules to accomplish learning in physical networks such as mechanical, flow, or electrical networks. In contrast to certain natural neural networks, however, such approaches have so far been restricted to the quasistatic limit, where they learn on time scales slow compared to their physical relaxation. This quasistatic constraint slows down learning, limiting the use of these methods as machine learning algorithms, and potentially restricting physical networks that could be used as learning platforms. Here we explore learning in an electrical resistor network that implements coupled learning, both in the lab and on the computer, at rates that range from slow to far above the quasistatic limit. We find that up to a critical threshold in the ratio of the learning rate to the physical rate of relaxation, learning speeds up without much change of 1

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Effect of directed aging on nonlinear elasticity and memory formation in a material

Cited by 32 publications

References 48 publications

Residual stresses and shear-induced overaging in boehmite gels

Residual stresses and shear-induced overaging in boehmite gels

Periodic training of creeping solids

Physical learning beyond the quasistatic limit

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