Self Driving

Self-driving laboratories (SDLs) automate scientific experimentation, aiming to accelerate discovery and optimize resource use by integrating robotics, artificial intelligence, and machine learning. Current research focuses on developing efficient algorithms for experimental design and data analysis, often employing Bayesian optimization and neural networks to guide experiments and extract meaningful insights from complex datasets. SDLs hold significant promise for enhancing efficiency and reproducibility across diverse scientific fields, from materials science and chemistry to space biology, by enabling high-throughput experimentation and autonomous optimization of experimental parameters.