Autonomous mobile robots for exploratory synthetic chemistry

Area of Science:

• Chemistry
• Robotics
• Artificial Intelligence

Background:

• Autonomous laboratories accelerate chemical synthesis but often rely on limited characterization data.
• Existing systems use bespoke equipment and single measurement techniques, hindering comprehensive analysis.
• Manual experiments utilize diverse instruments for decision-making, a capability lacking in current autonomous systems.

Purpose of the Study:

• To integrate mobile robots into an autonomous laboratory workflow for human-like decision-making.
• To enable robots to share existing laboratory equipment with human researchers.
• To develop a modular system for autonomous chemical synthesis and characterization.

Main Methods:

• A modular workflow combining mobile robots, an automated synthesis platform, liquid chromatography-mass spectrometry, and nuclear magnetic resonance spectroscopy.
• Utilizing a heuristic decision-maker to process orthogonal measurement data for reaction selection and reproducibility checks.
• Implementing mobile robots for equipment operation and human-like decision-making.

Main Results:

• Demonstrated successful integration of mobile robots for autonomous laboratory operations.
• Enabled robots to share existing equipment without monopolization or extensive redesign.
• Successfully applied the approach to structural diversification, supramolecular host-guest chemistry, and photochemical synthesis.
• Extended the method to an autonomous function assay for supramolecular host-guest binding properties.

Conclusions:

• This strategy enables autonomous laboratories to mimic human experimental decision-making using diverse data.
• The modular, robot-integrated system enhances efficiency and reproducibility in exploratory chemistry.
• The approach is particularly effective for complex synthesis yielding multiple products, including functional assays.