  Hydrogen exhibits unusual behaviors at megabar pressures, with consequences
for planetary science, condensed matter physics and materials science.
Experiments at such extreme conditions are challenging, often resulting in
hard-to-interpret and controversial observations. We present a theoretical
study of the phase diagram of dense hydrogen, using machine learning to
overcome time and length scale limitations while describing accurately
interatomic forces. We reproduce the re-entrant melting behavior and the
polymorphism of the solid phase. In simulations based on the machine learning
potential we find evidence for continuous metallization in the liquid, as a
first-order liquid-liquid transition is pre-empted by freezing. This suggests a
smooth transition between insulating and metallic layers in giant gas planets,
and reconciles existing discrepancies between experiments as a manifestation of
supercritical behavior.
