Autonomous experiments using active learning and AI

Active learning and automation will not easily liberate humans from laboratory workflows. Before they can really impact materials research, artificial intelligence systems will need to be carefully set up to ensure their robust operation and their ability to deal with both epistemic and stochastic errors. As autonomous experiments become more widely available, it is essential to think about how to embed reproducibility, reconfigurability and interoperability in the design of autonomous labs.

Automated summary

To apply automated experiments and artificial intelligence systems in materials research, it is necessary to ensure their stability, ability to handle cognitive and random errors, and incorporate reproducibility, reconfigurability, and interoperability in autonomous lab design.