Researchers have created Fast-Cat, a 'self-driving' lab, which leverages AI for efficient catalytic reaction analysis.

Fast-Cat significantly outperforms traditional testing, offering more data in five days than what's typically gathered in six months.

The system autonomously navigates and maps reactions, swiftly pinpointing optimal conditions and generating data for machine learning.

Its adaptive AI learns from each experiment, enhancing understanding of catalysis parameters' impact on reaction outcomes.

Fast-Cat's automation allows it to handle high-temperature, high-pressure gas-liquid reactions, which is a step up from conventional methods.

Industry recognition comes from companies like Eastman for Fast-Cat's role in optimizing catalysis processes, thus boosting efficiency and reducing costs.

Both the software and hardware of Fast-Cat are publicly accessible, encouraging widespread adoption in catalysis research across academia and industry.

The research findings are detailed in the paper published in Nature Chemical Engineering titled 'Autonomous reaction pareto-front mapping with a self-driving catalysis lab.'