Machine learning is disrupting multiple fields of human endeavor: healthcare, transportation, finance, communications, etc. Materials design is no exception in this disruption. Data-driven approaches can access the information embedded in years of experiments, perform rapid optimization of high-dimensional experimental conditions and design parameters, or design new molecules automatically. The Gomez-Bombarelli group at MIT combines cutting-edge machine learning models on experimental data with automation in physics-based atomistic simulations (molecular dynamics, electronic structure) to rapidly design and optimize new materials in multiple areas, such as: inverse chemical design of small molecules (drug-like molecules that optimally bind biological sites, organic-light emitting diode emitters, and organic battery electrolytes); virtual discovery of soft materials (lithium-conducting polymers and OLED transport materials); and chemical reactivity in the condensed phase (zeolite design for catalysis and chemical and thermal stability of organic electronics). There is great interest in using machine learning as the connector between multiple time and length scales: from electronic structure, to atomistic molecular dynamics, to coarse-grained models.