Dielectric properties of polymer nanocomposite interphases from electrostatic force microscopy using machine learning

Interphase regions in polymer nanocomposite materials are difficult to characterize due to their nano-scale di-mensions. Electrostatic force microscopy (EFM) provides a pathway to local dielectric property measurements, but extracting local dielectric permittivity in complex interphase geometries from EFM measurements remains a challenge. We demonstrate the efficacy of machine learning (ML) models to extract interphase permittivity using a data set of synthetic EFM force gradient scans generated by finite element simulations. We show that both support vector regression (SVR) and random forest (RF) algorithms are able to ?invert? the force gradient scan to predict the permittivity with high accuracy. Feature reduction by principal component analysis (PCA) improves the model?s performance and reveals force gradient contrast to be the most important feature in permittivity detection. We find that these ML models perform better than analytical approaches by capturing significant geometric complexity of EFM measurements.

Automated summary

The study demonstrates the effectiveness of machine learning in extracting interphase permittivity in polymer nanocomposite materials. Machine learning models outperform analytical approaches by capturing significant geometric complexity and improving the accuracy of permittivity prediction.