Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing

© 2018 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 ten times 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-performance computing 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, with considerable first-mover advantages at stake. The convergence of high-performance computing, automation, and machine learning promises to accelerate the rate of materials discovery by ≥10 times, better aligning investor and stakeholder timelines. Infrastructure and human-capital investments are discussed, including equipment capabilities, data management, education, and incentives. As our field transitions from thinking “data poor” to thinking “data rich,” we envision a scientific laboratory where the process of materials discovery continues without disruptions, aided by computational power augmenting the human mind, and freeing the latter to perform research closer to the speed of imagination, addressing societal challenges in market-relevant timeframes. A combination of emergent technologies promises to accelerate novel materials development by ten times or more: tool automation, high-performance computing, and machine learning. We describe state-of-the-art attempts to realize this vision and identify resource gaps, including required infrastructure and human-capital investments