Framework for a High-Throughput Screening Method to Assess Polymer/Plasticizer Miscibility: The Case of Hydrocarbons...

Area of Science:

• Polymer Science
• Materials Science
• Computational Chemistry

Background:

• Polymer composite materials require plasticizers (PLs) to enhance processability by lowering the glass transition temperature.
• The effectiveness of PLs depends heavily on their miscibility with the polymer matrix, which is influenced by PL characteristics like size, topology, and flexibility.
• The vast number of potential PLs makes traditional trial-and-error screening inefficient.

Purpose of the Study:

• To identify reliable topological and thermodynamic descriptors for predicting plasticizer miscibility in polymers.
• To develop a computationally inexpensive method for screening plasticizers using molecular simulations.
• To establish a machine learning model for automating the plasticizer screening process.

Main Methods:

• Utilized coarse-grained molecular simulations on a dataset of 48 plasticizers.
• Established correlations between plasticizer topology, internal flexibility, aggregation thermodynamics, and miscibility.
• Developed and validated a decision tree model using these descriptors for classifying plasticizers as miscible or immiscible.

Main Results:

• Identified key topological and thermodynamic descriptors that accurately predict plasticizer miscibility.
• The decision tree model achieved a high F1 score of 0.86 ± 0.01 via cross-validation, demonstrating strong predictive performance.
• The developed procedure enables a 10-fold reduction in the screening test space for plasticizers.

Conclusions:

• Machine learning, based on molecular simulation descriptors, offers a promising route for automated plasticizer screening.
• The identified descriptors and methodology can accelerate the development of efficient workflows for selecting plasticizers for various polymers, including polyolefins.
• Further research may be needed to adapt descriptors for systems where polar interactions dominate miscibility.