Machine-learned force fields (MLFFs) can bring first-principles accuracy to finite-temperature molecular dynamics for materials simulations. Here, the authors use MLFFs, trained on-the-fly using only ground-state structures, to predict phase transitions in the prototypical ferroelectrics BaTiO, PbTiO, LiNbO, and BiFeO. Order parameter discontinuities, mixed order–disorder and displacive character, and space groups are correctly predicted, while exact transition temperatures are functional-dependent. This demonstrates both the promise and current limitations of MLFFs for simulating the thermal evolution of materials, where long-range electrostatic interactions and collective lattice instabilities are imperative to the physics and phase transitions. Ferroelectrics thus constitute a stringent testbed for MLFFs.