Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz

Corneel Casert; Tom Vieijra; Stephen Whitelam; Isaac Tamblyn

doi:10.1103/physrevlett.127.120602

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arXiv · 2011.08657

Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz

Corneel Casert·Tom Vieijra·Stephen Whitelam·Isaac Tamblyn

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Abstract

We use a neural network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones. We obtain the scaled cumulant-generating function for the dynamical activity of the Fredrickson-Andersen model, a prototypical kinetically constrained model, in one and two dimensions, and present the first size-scaling analysis of the dynamical activity in two dimensions. These results provide a new route to the study of dynamical large-deviation functions, and highlight the broad applicability of the neural-network state ansatz across domains in physics.

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Corneel Casert, Tom Vieijra, Stephen Whitelam, Isaac Tamblyn. 2020-11-17. Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz. https://doi.org/10.1103/physrevlett.127.120602

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