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

TL;DR

This paper discusses how integrating automation, machine learning, and high-performance computing can significantly accelerate materials development, reducing timelines and increasing success rates for innovations impacting energy, healthcare, and the environment.

Contribution

It presents a comprehensive framework combining automation, HPC, and machine learning to transform materials research and identifies key resource gaps for future progress.

Findings

Automation enables rapid experimental testing.

HPC predicts properties to focus experiments.

Machine learning refines theories and guides next steps.

Abstract

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.