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Machine Learning Framework for Evaluating Microscale Interface Cohesive Zone Models of Polymer Composites.

Zheng Li¹, Juan Du¹, Hui Yi¹

¹Department of Engineering Mechanics, Dalian University of Technology, Dalian, Liaoning 116024, China.

ACS Applied Materials & Interfaces

|November 3, 2025

Summary

This study introduces a machine learning-molecular dynamics framework to predict cohesive zone model parameters for polymer composites. This scalable method reduces computational costs and reliance on traditional molecular dynamics simulations.

Keywords:

Cohesive Zone Model Machine learning Microscale interface Molecular Dynamics Multiscale

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Area of Science:

Materials Science
Computational Mechanics
Polymer Composites

Background:

Cohesive zone models (CZM) are crucial for multiscale simulations of polymer composites.
Traditional molecular dynamics (MD) methods for calculating CZM parameters face computational and scalability challenges.

Purpose of the Study:

To develop a machine learning (ML)-MD framework for predicting CZM parameters in polymer composite interfaces.
To establish a scalable methodology for preliminary interface assessment in multiscale analysis.

Main Methods:

Proposed an innovative decoupling simulation method to simplify complex interfacial interactions.
Identified van der Waals interactions and hydrogen bonds as key factors influencing CZM parameters.
Integrated ML with MD for efficient parameter prediction.

Main Results:

Successfully predicted CZM parameters for microscale interfaces in polymer composites.
Validated predicted parameters against experimental and literature data.
Applied the framework to finite element simulations of fiber pull-out behavior.

Conclusions:

The ML-MD framework offers a scalable and computationally efficient alternative to conventional MD for CZM parameter calculation.
This methodology reduces reliance on extensive MD workflows for interface property assessment.
Enables accurate input for microscale interface properties in multiscale simulations.