Automated systems perform repetitive tasks quickly and consistently with minimal human intervention. These characteristics are particularly effective in manufacturing, where speed and reproducibility drive down cost and increase quality. In materials science laboratories, however, automation is rarely used. The diverse experimental workflows used in materials science often require highly customized automation that is not commercially available. Even when automated tools for materials synthesis or characterization are available, they are often linked together by manual steps. “Flexible automation” is changing this situation because of the recent emergence of safer, cheaper, and more user-friendly robotics. I define flexible automation as the concept of using robotics to create reconfigurable automated experiments. A powerful feature of flexible automation is the ability to program multiple experimental procedures together into fully automated workflows. These experimental procedures can be performed by commercially-available or customized hardware modules that operate on samples either serially or in parallel. Flexible automation platforms can be readily reconfigured to the changing needs of the experiment. It is for these reasons that flexible automation can accelerate the unique experiments characteristic to materials science. As a proof-of-principle set of experiments, I will show how Ada, our self-driving laboratory for thin-film materials, discovers and optimizes high-performance, low-cost hole-transport materials for use in advanced solar cells. I will also showcase how Ada’s modular design can enable the automated and autonomous discovery of materials for other clean energy technologies.

<i>Figure from Nat. Mater., <b>2022</b>, 21, 722–726</i>

Figure from Nat. Mater., 2022, 21, 722–726

Curtis Berlinguette

The University of British Columbia

Appearances