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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform.

Anirudh M K Nambiar¹, Christopher P Breen², Travis Hart¹

¹Department of Chemical Engineering, Massachusetts Institute of Technology,77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.

ACS Central Science

|June 27, 2022

Summary

This summary is machine-generated.

This study integrates computer-aided synthesis planning (CASP) with robotic flow synthesis and machine learning. Automation and human expertise optimize complex chemical synthesis, enhancing experimental design and execution.

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Area of Science:

Organic Chemistry
Chemical Engineering
Automation and Robotics

Background:

Computer-aided synthesis planning (CASP) tools require experimental data for process optimization.
Current methods necessitate extensive manual experimentation to refine synthesis routes.

Purpose of the Study:

To demonstrate the integration of CASP, robotic flow synthesis, and machine learning for optimizing chemical synthesis.
To enhance experimental design, execution, and optimization through automation and data-rich experimentation.

Main Methods:

Utilized a modular, robotic flow synthesis platform with integrated process analytical technology (PAT).
Employed multi-objective Bayesian optimization to identify optimal process variables.
Incorporated human insights to address catalyst deactivation and refine reaction conditions.

Main Results:

Successfully planned and optimized a multistep synthesis route for sonidegib.
Demonstrated the platform's modularity and reconfigurability for flexible synthesis.
Showcased the ability to control residence time and order of addition for improved yield.

Conclusions:

Automation, machine learning, and robotics significantly enhance manual experimentation in chemical synthesis.
Integrated systems accelerate the optimization of complex organic molecule synthesis.
This approach provides a data-rich framework for advancing chemical process development.