Many aspects of the phase diagram of dense hydrogen remain poorly understood,
sometimes even qualitatively. Dense hydrogen is predicted become a high-temperature
superconductor at sufficiently high pressure and is crucial in determining the
structure of gas giant planets. Addressing the entire phase diagram with accurate
ab initio simulations like diffusion Monte Carlo (DMC) is not currently feasible
due to the computational cost, which limits studies to small system sizes.
Recently, machine-learned interatomic potentials trained on ab initio data have
been applied in large-scale molecular dynamics simulations to approach the accuracy
of the ab initio methods without the finite size errors. Typically, these have relied
on density functional theory to generate the training data. Here we provide the a
large-scale DMC database for dense hydrogen, which allows training of machine-learned
potentials with DMC accuracy.