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January 25, 2026Polymer Composites0 citations

Machine Learning Prediction of Tribological Properties of Graphene‐Reinforced Perfluoroelastomer Composites Based on Molecular Dynamics Simulations and Experimental Research

SJShengbo JinShenyang University of Technology JZJing ZhaoShenyang University of Technology XTXueying TanChina Academy Of Machinery Science & Technology (China)

Key Points

This research aims to predict tribological properties of graphene-reinforced perfluoroelastomer composites using molecular dynamics and machine learning.
Integrated framework combining molecular dynamics simulations, machine learning, and experimental research.
Analysis of tribological properties under varying temperature, normal load, and sliding velocity conditions.
Statistical modeling using a novel SDM algorithm for predictive accuracy.
Negative correlation found between friction coefficient and temperature, normal load, and sliding velocity.
Average diffusion coefficient increased from 0.00422 to 0.04300 with rising sliding velocity.
Normal load increased transfer film thickness by 39.13% and reduced surface roughness by 36.19%.
SDM model achieved prediction accuracy of R² = 0.9662, outperforming conventional algorithms.
Minimum friction coefficient recorded was 1.339 under six different test conditions.

Abstract

ABSTRACT This study aims to develop high‐performance polymer nanocomposites based on perfluoroelastomers (FFKM) using an integrated framework that combines molecular dynamics (MD) simulations, machine learning (ML), and experiments to investigate the factors governing tribological behavior. The friction coefficient shows negative correlations with temperature, normal load, and sliding velocity. Increasing the sliding velocity raises the average diffusion coefficient from 0.00422 to 0.04300. Increasing the normal load increases the transfer film thickness by 39.13% and reduces surface roughness by 36.19%. The SDM model achieves the highest prediction accuracy (R 2 = 0.9662), outperforming conventional algorithms. Among six friction test conditions, the minimum friction coefficient is 1.339. Two conditions with identical PV values of 0.1 exhibit similar friction coefficients (1.604 and 1.646). The experimental trends are consistent with the MD and ML results.

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