Successful materials innovations can transform society. However, materials
research often involves long timelines and low success probabilities,
dissuading investors who have expectations of shorter times from bench to
business. A combination of emergent technologies could accelerate the pace of
novel materials development by 10x or more, aligning the timelines of
stakeholders (investors and researchers), markets, and the environment, while
increasing return-on-investment. First, tool automation enables rapid
experimental testing of candidate materials. Second, high-throughput computing
(HPC) concentrates experimental bandwidth on promising compounds by predicting
and inferring bulk, interface, and defect-related properties. Third, machine
learning connects the former two, where experimental outputs automatically
refine theory and help define next experiments. We describe state-of-the-art
attempts to realize this vision and identify resource gaps. We posit that over
the coming decade, this combination of tools will transform the way we perform
materials research. There are considerable first-mover advantages at stake,
especially for grand challenges in energy and related fields, including
computing, healthcare, urbanization, water, food, and the environment.Comment: 22 pages, 3 figure
