Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter pace of discovery requires faster research and development cycles enabled by better integration between hypothesis generation, design, experimentation, and data analysis. Typical research cycles take months to years. However, data-driven automated laboratories, or self-driving laboratories, can significantly accelerate molecular and materials discovery. Recently, substantial advancements have been made in the areas of machine learning and optimization algorithms that have allowed researchers to extract valuable knowledge from multidimensional data sets. Machine learning…

• SN
  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

  Award: 191127
• UO
  University of Toronto
• DA
  Defense Advanced Research Projects Agency

  Award: HR00111920027
• NR
  National Research Council Canada

  Award: MCF-106
• NR
  Natural Resources Canada
• OO
  Office of Naval Research

  Awards: N00014-21-1-2137, N00014-19-1-2134