AIR-Chem: Authentic Intelligent Robotics for Chemistry

Area of Science:

• Chemistry
• Robotics
• Artificial Intelligence
• Materials Science

Background:

• Artificial intelligence (AI) and robotics are transforming materials discovery.
• Current laboratory processes can be time-consuming and require significant human intervention.

Purpose of the Study:

• To introduce AIR-Chem, an intelligent robotics platform for chemistry.
• To demonstrate the capability of AIR-Chem in autonomous material synthesis and analysis.
• To validate an improved nucleation theory for inorganic perovskite quantum dots (IPQDs).

Main Methods:

• Integration of AI, robotics, gradient descent optimization, multiple external field modulations, computer vision (CV), and automated guided vehicle (AGV) components.
• Development of a portable, cloud-computing-enabled, remotely controllable robotic system.
• Implementation of standalone laboratory operations with learning capabilities for parametric procedures.

Main Results:

• AIR-Chem demonstrated high reproducibility and precision in laboratory operations.
• The system successfully learned and executed procedures for target material synthesis.
• An improved nucleation theory for IPQDs was theoretically proposed and experimentally validated using AIR-Chem.

Conclusions:

• AIR-Chem facilitates the advancement of unmanned chemistry laboratories.
• The platform streamlines chemical synthesis and physical property analysis.
• This work showcases the future of chemistry driven by AI and robotics.