From coarse-grained molecular dynamics to machine learning: A comparative study of modeling techniques for predicting polycarbonate maximum stress from structural features

This study explores the use of machine learning to predict the maximum stress of polycarbonate materials based on key structural parameters: polydispersity, radius of gyration, and molecular entanglements. These parameters are derived from coarse-grained molecular dynamics simulations of transverse constrained tension. We compare various machine learning algorithms, including multivariate linear regression, log–log regression, gradient boosted regression trees, extreme gradient boosted regression trees, and artificial neural networks. Model performance is evaluated using the coefficient of determination R 2 , root-mean-squared error, and computational efficiency. While traditional linear and log–log models achieve R 2 values of 0.77 and 0.83, respectively, boosted regression trees and artificial neural networks attain higher R 2 values of approximately 0.90. However, artificial neural networks require over four orders of magnitude more computation time compared to boosted regression tree models, which has R 2 of 0.89. To interpret feature importance, we employ Shapley additive explanations analysis on the extreme gradient boosted model. The radius of gyration is identified as the most influential parameter, highlighting its role in maintaining structural integrity during tensile deformation. A larger radius of gyration enhances load distribution along molecular chains, ensuring uniform stress transfer and improved mechanical performance. • Machine learning predicted maximum stress from coarse-grained simulation data. • Machine learning offered a faster alternative to molecular dynamics simulation. • Five different algorithms are assessed in terms of accuracy and efficiency. • Shapley explanations revealed the effects of structure on material properties. • The approach helps accelerate design by predicting material properties.