Lawrence Berkeley National Laboratory – Efficient Parameter Control with Chemspeed’s AUTOPLANT

Mobile robotic chemists are a fast growing trend in the field of chemistry and materials research. However, so far these mobile robots lack workflow awareness skills. 

The integration of automated synthesis and machine learning (ML) is transforming analytical chemistry by enabling data-driven approaches to method development. Chromatographic column selection, a critical yet time-consuming step in separation science, stands to benefit substantially from such advances.

The development of a general autonomous platform for organic synthesis that enables faster, flexible and efficient delivery of target molecules is an attractive strategy for many fields such as drug discovery and material sciences.