  A cardinal obstacle to performing quantum-mechanical simulations of
strongly-correlated matter is that, with the theoretical tools presently
available, sufficiently-accurate computations are often too expensive to be
ever feasible. Here we design a computational framework combining
quantum-embedding (QE) methods with machine learning. This allows us to bypass
altogether the most computationally-expensive components of QE algorithms,
making their overall cost comparable to bare Density Functional Theory (DFT).
We perform benchmark calculations of a series of actinide systems, where our
method describes accurately the correlation effects, reducing by orders of
magnitude the computational cost. We argue that, by producing a larger-scale
set of training data, it will be possible to apply our method to systems with
arbitrary stoichiometries and crystal structures, paving the way to virtually
infinite applications in condensed matter physics, chemistry and materials
science.
