Machine Learning-Enabled Nanoscale Phase Prediction in Engineered Poly(Vinylidene Fluoride)

Engineered poly(vinylidene fluoride) (PVDF) with its diverse crystalline phases plays a crucial role in determining the performance of devices in piezo-, pyro-, ferro- and tribo-electric applications, indicating the importance of distinct phase-detection in defining the structure-property relation. However, traditional characterization techniques struggle to effectively distinguish these phases, thereby failing to offer complete information. In this study, multimodal data-driven techniques have been employed for distinguishing different phases with a machine learning (ML) approach. This developed multimode model has been trained from empirical to theoretical data and demonstrates a classification accuracy of >94%, 15% more noise resilience, and 11% more accuracy from unimodality. Thus, from conception to validation, an alternative approach is provided to autonomously distinguish the different PVDF phases and eschew repetitive experiments that saved resources, thus accelerating the process of materials selection in various applications.