  Recent applications of machine learning and statistical inference provide
case studies demonstrating how such approaches can accelerate the discovery
process in physical chemistry and related fields. Examples discussed in this
review include the introduction of automated approaches to systematically
improve experimental design, increase the efficiency of computationally
expensive molecular simulations, facilitate construction of predictive models
for complex biological processes, and discover interparticle potentials that
lead to materials which meet specified design goals. A common theme is the
synergy between experiment and computation enabled by such approaches.
