  Ab initio simulations are capable of providing detailed information of
material behavior at the nanoscale. Simulating experimentally relevant
situations is, however, often computationally intense. Using hybrid approaches
between ab initio methods such as density functional theory (DFT) and machine
learning, new models can be constructed which retain quantum accuracy while
being computationally faster by several orders of magnitude. Two examples are
discussed in this paper. The first is the computational search for low energy
substitutional defect complexes in silicon. The second is the construction of
deep learning potentials for ab initio-level molecular dynamics simulations.
The latter is applied to reproduce the 0 K equation of state and high
temperature thermal expansion of Si using the same model.
