  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.
