The performance of many advanced technologies are inhibited by the slow process of optimizing materials. Physics-based theories and computational tools are proven routes to accelerating materials design but are unavailable or computationally-intractable for many problems. In this talk, we demonstrate how machine learning (ML) can close this capability gap. We start with a discussion of how to use machine learning on different types of materials data, with an emphasis on creating tools to replace costly Density-Functional Theory calculations. We then will describe several case studies – including metallic glass design and radiation damage prediction – to show the advantages and pitfalls of using ML to design materials. Finally, we conclude with an overview of the data infrastructure and software efforts designed to make ML capabilities readily-available to the wider scientific community.