  The integration of robots in chemical experiments has enhanced experimental
efficiency, but lacking the human intelligence to comprehend literature, they
seldom provide assistance in experimental design. Therefore, achieving
full-process autonomy from experiment design to validation in self-driven
laboratories (SDL) remains a challenge. The introduction of Generative
Pre-trained Transformers (GPT), particularly GPT-4, into robotic
experimentation offers a solution. We introduce GPT-Lab, a paradigm that
employs GPT models to give robots human-like intelligence. With our robotic
experimentation platform, GPT-Lab mines literature for materials and methods
and validates findings through high-throughput synthesis. As a demonstration,
GPT-Lab analyzed 500 articles, identified 18 potential reagents, and
successfully produced an accurate humidity colorimetric sensor with a root mean
square error (RMSE) of 2.68%. This showcases the rapid materials discovery and
validation potential of our system.
