Artificial intelligence offers a new and broad range of tools for materials design that require a new suite of experimental and simulation tools to be successfully employed. In particular, data must be made available in larger quantities than what is produced by conventional experiments, and communicated in forms accessible to computer software as well as humans. Additionally, the increased rates at which machine learning algorithms can adapt to new data and generate improved predictions also necessitate techniques that can evaluate material properties just as quickly. In this symposium, we propose to highlight research that demonstrates the full process of machine-learning-driven materials design: from data acquisition through model development to materials design. Of particular interest will be studies that demonstrate unique approaches for leveraging the unique capabilities of artificial intelligence, which illustrate the possible transformative role of AI will play in materials research.