Principal deuterium Hugoniot via quantum Monte Carlo and Δ -learning

We present a study of the principal deuterium Hugoniot for pressures up to 150 GPa, using machine learning potentials (MLPs) trained with quantum Monte Carlo (QMC) energies, forces, and pressures. In particular, we adopted a recently proposed workflow based on the combination of Gaussian kernel regression and -learning. By fully taking advantage of this method, we explicitly considered finite-temperature electrons in the dynamics, whose effects are highly relevant for temperatures above 10 kK. The Hugoniot curve obtained by our MLPs shows a good agreement with the most recent experiments, particularly in the region below 60 GPa. At larger pressures, our Hugoniot curve is slightly more compressible than the one yielded by experiments, whose uncertainties generally increase, however, with pressure. Our work demonstrates that QMC can be successfully combined with -learning to deploy reliable MLPs for complex extended systems across different thermodynamic conditions, by keeping the QMC precision at the computational cost of a mean-field calculation.