AI Self-Driving Laboratories Accelerate Scientific Research, Boosting Experimental Efficiency Up to 30-Fold

Self-driving laboratories, or SDLs, combine AI, robotics and automated equipment to connect hypothesis generation, experimental design, execution and analysis across the scientific research process. These systems can reduce repeated manual trial and error, making them particularly important for shortening research and development cycles for new drugs and materials.

The latest research shows that SDLs can reach conclusions using about one-thirtieth as many experiments as conventional methods, boosting experimental efficiency by up to 30-fold. Reports did not identify the research institution, publication date or investment amount. The findings nevertheless indicate that automated, closed-loop research and development has significant potential to save time and resources.