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Harnessing data and control with AI/ML-driven polymerization and copolymerization.

Rigoberto Advincula1, Ilia Ivanov1, Rama Vasudevan1

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Summary
This summary is machine-generated.

Artificial intelligence and machine learning (AI/ML) can optimize polymer synthesis. This approach uses continuous flow reactors and real-time monitoring for precise control over copolymerization processes.

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Area of Science:

  • Materials Science
  • Polymer Chemistry
  • Artificial Intelligence

Background:

  • Traditional methods for polymer development are often slow and rely on trial-and-error.
  • Improving properties of existing polymers is crucial for various applications.
  • AI/ML offers a powerful approach to accelerate materials discovery and optimization.

Purpose of the Study:

  • To demonstrate AI/ML protocols for optimizing polymerization and copolymerization.
  • To achieve targeted polymer properties without extensive reformulation.
  • To establish a framework for autonomous polymer fabrication.

Main Methods:

  • Utilizing AI/ML for optimizing synthesis and manufacturing.
  • Employing self-driving continuous flow chemistry reactors with integrated sensors.
  • Implementing real-time ML with online monitoring and feedback loops.
  • Refining classical equations like the Mayo-Lewis equation (MLE) using ML.

Main Results:

  • Demonstrated protocols for hierarchical optimization of polymerization via AI/ML.
  • Initial results show ML refinement of the MLE and analysis of time-series data.
  • Established an autonomous flow reactor system as a data-generating platform.

Conclusions:

  • AI/ML enables precision control over copolymerization processes.
  • Autonomous, AI-guided protocols can lead to the future fabrication of novel polymers.
  • This work lays the groundwork for data-driven, accelerated materials science.