Free-then-freeze: transient learning degrees of freedom for introducing function in materials

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.

9 pages, 5 figures

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• Testing theories of the glass transition with the same liquid, but many kinetic rules
• Creating equilibrium glassy states via random particle bonding
• Exceptionally Dense and Resilient Polydisperse Disk Packings
• The Ideal Glass and the Ideal Disk Packing in Two Dimensions
• Bifurcation instructed design of multistate machines
• Irreversible swap algorithms for soft sphere glasses
• Particle pinning as a method to manipulate marginal stability
• Yielding in dense active matter