  Propelled partly by the Materials Genome Initiative, and partly by the
algorithmic developments and the resounding successes of data-driven efforts in
other domains, informatics strategies are beginning to take shape within
materials science. These approaches lead to surrogate machine learning models
that enable rapid predictions based purely on past data rather than by direct
experimentation or by computations/simulations in which fundamental equations
are explicitly solved. Data-centric informatics methods are becoming useful to
determine material properties that are hard to measure or compute using
traditional methods--due to the cost, time or effort involved--but for which
reliable data either already exists or can be generated for at least a subset
of the critical cases. Predictions are typically interpolative, involving
fingerprinting a material numerically first, and then following a mapping
(established via a learning algorithm) between the fingerprint and the property
of interest. Fingerprints may be of many types and scales, as dictated by the
application domain and needs. Predictions may also be extrapolative--extending
into new materials spaces--provided prediction uncertainties are properly taken
into account. This article attempts to provide an overview of some of the
recent successful data-driven "materials informatics" strategies undertaken in
the last decade, and identifies some challenges the community is facing and
those that should be overcome in the near future.
