Machine learning a dynamical phase diagram for many-body localization

We analyze the dynamical phase diagram of a 1-dimensional disordered and interacting spin-chain with a many-body localization transition, using a recurrent neural network trained on magnetization dynamics. The obtained phase diagram shows good agreement with previously known results obtained from time-dependent data and entanglement spectra, but has was obtained using dynamics of only physically measurable quantities, namely the magnetization of the spins obtained from exact time evolution.