Memory behavior of a randomly driven model glass

Abstract

We investigate by atomistic simulations the memory behavior a model glass subjected to random driving protocols. The training consists of a random walk of forward and/or backward shearing sequences bounded by a maximal shear strain of absolute value γT . We show that such a stochastic training protocol is able to record the training amplitude. Different read-out protocols are also tested and are shown to be able to retrieve the training amplitude. We then emphasize the ten- sorial character of the memory encoded in the glass sample and then characterize the anisotropic mechanical behavior of the trained samples.

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