GFN-xTB-Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization.

Area of Science:

• Polymer Chemistry
• Computational Chemistry
• Materials Science

Background:

• Radiation-induced graft polymerization (RIGP) is crucial for modifying polymer properties.
• Understanding monomer behavior in emulsion is key to controlling RIGP.
• Accurate prediction of RIGP requires detailed molecular insights.

Purpose of the Study:

• To compute key molecular parameters for methacrylate monomers in emulsion.
• To develop predictive models for RIGP using computational data.
• To identify critical factors influencing RIGP through machine learning.

Main Methods:

• Utilized the Conformer-Rotamer Ensemble Sampling Tool (CREST) package.
• Employed semiempirical GFN-xTB level calculations for molecular properties.
• Developed machine learning models based on CREST-derived parameters.

Main Results:

• Obtained explicit solvation free energies, conformational entropy, monomer radius, and dipole moments.
• Demonstrated the dynamic nature of methacrylate monomers under emulsion conditions.
• Built effective prediction models for RIGP with chemically interpretable factors.

Conclusions:

• CREST package provides dynamic insights into monomers for RIGP.
• Computational parameters from CREST are valuable for RIGP prediction models.
• Machine learning models offer effective and interpretable reactivity predictions for RIGP.