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Recent applications of machine learning and statistical inference providecase studies demonstrating how such approaches can accelerate the discoveryprocess in physical chemistry and related fields. Examples discussed in thisreview include the introduction of automated approaches to systematicallyimprove experimental design, increase the efficiency of computationallyexpensive molecular simulations, facilitate construction of predictive modelsfor complex biological processes, and discover interparticle potentials thatlead to materials which meet specified design goals. A common theme is thesynergy between experiment and computation enabled by such approaches.

Recent advances in accelerated discovery through machine learning and statistical inference