Machine learning has the potential to revolutionize the discovery and development process of novel materials.¹ However, to properly train these models, researchers must have access to massive amounts of experimental data.

Automated systems provide a way to collect and process high-quality data at record speeds by reducing the need for manual operation and oversight. Recently, researchers at Alfred University in New York had the opportunity to explore the benefits of automated experimentation when Semilab (Budapest, Hungary) loaned the university its new IND-1500 nanoindentation system (Figure 1) in October 2023.