  Machine learning techniques have found their way into computational chemistry
as indispensable tools to accelerate atomistic simulations and materials
design. In addition, machine learning approaches hold the potential to boost
the predictive power of computationally efficient electronic structure methods,
such as density functional theory, to chemical accuracy and to correct for
fundamental errors in density functional approaches. Here, recent progress in
applying machine learning to improve the accuracy of density functional and
related approximations is reviewed. Promises and challenges in devising machine
learning models transferable between different chemistries and materials
classes are discussed with the help of examples applying promising models to
systems far outside their training sets.
