Researchers are developing "self-driving laboratories" that combine robotics, artificial intelligence, and automated high-throughput experimentation to accelerate molecular discovery and material optimization. These systems can explore vast experimental spaces more efficiently than traditional trial-and-error approaches, using less material and time while potentially democratizing access to new materials research. Two recent Perspectives analyze both the technological strengths and practical limitations of these automated discovery platforms.
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