  This paper reviews past and ongoing efforts in using high-throughput ab-inito
calculations in combination with machine learning models for materials design.
The primary focus is on bulk materials, i.e., materials with fixed, ordered,
crystal structures, although the methods naturally extend into more complicated
configurations. Efficient and robust computational methods, computational
power, and reliable methods for automated database-driven high-throughput
computation are combined to produce high-quality data sets. This data can be
used to train machine learning models for predicting the stability of bulk
materials and their properties. The underlying computational methods and the
tools for automated calculations are discussed in some detail. Various machine
learning models and, in particular, descriptors for general use in materials
design are also covered.
