Showing papers by "Sidney R. Nagel published in 2021"
TL;DR: In this article, it was shown that adding interactions between rearranging clusters overcomes these deficiencies and allows simultaneous encoding of multiple memories, which would not have been possible otherwise. But it does not account for the long training times and multi-period orbits observed in simulated sheared packings.
Abstract: Cyclically sheared jammed packings form memories of the shear amplitude at which they were trained by falling into periodic orbits where each particle returns to the identical position in subsequent cycles. While simple models that treat clusters of rearranging particles as isolated two-state systems offer insight into this memory formation, they fail to account for the long training times and multiperiod orbits observed in simulated sheared packings. We show that adding interactions between rearranging clusters overcomes these deficiencies. In addition, interactions allow simultaneous encoding of multiple memories, which would not have been possible otherwise. These memories are different in an essential way from those found in other systems, such as multiple transient memories observed in sheared suspensions, and contain information about the strength of the interactions.
15 citations
TL;DR: In this article, a correction to this paper has been published: https://doi.org/10.1038/s41586-021-03444-z.html
Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-03444-z.
6 citations
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TL;DR: In this paper, the authors introduce new numerical protocols for creating stable jammed packings that first introduce and subsequently remove degrees of freedom such as particle sizes or particle stiffnesses, which can push the system to much more stable and deeper energy minima.
Abstract: The mechanical stability of a physical system plays a crucial role in determining its excitations and response to strain. Recent advances have led to protocols that can create particularly stable amorphous solids. Such systems, whether they be physical systems created using vapor-deposition or numerical model systems created using swap or breathing algorithms, exist in exceptionally deep energy minima marked by the absence of low-frequency quasilocalized modes. We introduce new numerical protocols for creating stable jammed packings that first introduce and subsequently remove degrees of freedom such as particle sizes or particle stiffnesses. We find that different choices for the degrees of freedom can lead to very different results. For jammed packings, degrees of freedom that couple to the jamming transition, e.g., particle sizes, push the system to much more stable and deeper energy minima than those that only couple to interaction stiffnesses.
3 citations
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TL;DR: In this article, it was shown that in systems where the spring interaction strength is equal to the bond-reorientation interaction, the Poisson's ratio identically goes to zero and is independent of the network geometry.
Abstract: The Poisson's ratio of a spring network system has been shown to depend not only on the geometry but also on the relative strength of angle-bending forces in comparison to the bond-compression forces in the system. Here we derive the very simple analytic result that in systems where the spring interaction strength is equal to the bond-reorientation interaction, the Poisson's ratio identically goes to zero and is independent of the network geometry.
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TL;DR: In this paper, the authors introduce the concept of transient learning degrees of freedom, which can lead to novel material design and training protocols that guide a system into a desired metastable state.
Abstract: The introduction of transient learning degrees of freedom into a system can lead to novel material design and training protocols that guide a system into a desired metastable state. In this approach, some degrees of freedom, which were not initially included in the system dynamics, are first introduced and subsequently removed from the energy minimization process once the desired state is reached. Using this conceptual framework, we create stable jammed packings that exist in exceptionally deep energy minima marked by the absence of low-frequency quasilocalized modes; this added stability persists in the thermodynamic limit. The inclusion of particle radii as transient degrees of freedom leads to deeper and much more stable minima than does the inclusion of particle stiffnesses. This is because particle radii couple to the jamming transition whereas stiffnesses do not. Thus different choices for the added degrees of freedom can lead to very different training outcomes.