Autonomous Experimentation

Autonomous experimentation (AE) aims to automate the scientific process, using machine learning and robotic systems to design, execute, and analyze experiments with minimal human intervention. Current research focuses on developing robust workflow management systems, integrating human expertise through human-in-the-loop approaches, and employing Bayesian optimization and other machine learning algorithms like deep kernel learning to guide experimental design and efficiently explore complex parameter spaces. This accelerates scientific discovery across diverse fields, such as materials science and pharmaceutical research, by increasing throughput, improving safety, and enabling the exploration of larger design spaces than previously possible.