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Related Experiment Video

Updated: Jun 27, 2026

MALDI-ToF MS Method for the Characterization of Synthetic Polymers with Varying Dispersity and End Groups
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Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning.

Yunfei Wang1,2, Daniel Struble1, Saroj Upreti1

  • 1School of Polymer Science and Engineering, Center for Optoelectronic Materials and Devices, the University of Southern Mississippi, Hattiesburg, Mississippi 39406, United States.

JACS Au
|June 26, 2026
PubMed
Summary

Researchers developed a high-throughput workflow using machine learning to accelerate the discovery of supramolecular polymer blends (SPBs). This data-driven approach enables predictive models for designing SPBs with desired morphologies and properties.

Keywords:
ML-guided polymer designautomation and high-throughput materials discoveryhigh-throughput characterizationsupramolecular polymer blends

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Area of Science:

  • Materials Science
  • Polymer Science
  • Computational Chemistry

Background:

  • Supramolecular polymer blends (SPBs) possess tunable morphologies crucial for macroscopic properties.
  • Rational design of SPBs is hindered by a lack of predictive structure-morphology models.

Purpose of the Study:

  • To establish a data-driven, high-throughput workflow for accelerated discovery of supramolecular polymer blends.
  • To develop predictive models for correlating SPB structure with morphology.

Main Methods:

  • Modular synthesis of 33 hydrogen-bonding end-functional homopolymers.
  • Robotic formulation to create 260 SPBs.
  • Automated atomic force microscopy (AFM) for morphology characterization.
  • Machine learning (ML), specifically Support Vector Regression (SVR), for model training.

Main Results:

  • Generated 260 SPBs and 2340 AFM morphology datasets rapidly.
  • Developed an SVR model that accurately predicted phase-separation sizes.
  • Experimental validation confirmed the model's predictive accuracy for target sizes (50, 100, 150 nm).

Conclusions:

  • Coupling high-throughput experimentation with ML significantly accelerates morphology discovery in SPBs.
  • This work provides a large-scale dataset for supramolecular polymer systems.
  • The workflow enables more rational design of SPBs with targeted properties.