Using pretrained machine learning models to predict luminous and solar transmittance controllability of liquid crystal/polymer composites from microstructural images

Abstract

Polarized optical microscopy (POM) images of polymer network liquid crystals (PNLCs) were first analyzed using a pretrained machine learning model for feature extraction and hierarchical clustering. The analyses worked well in predicting and improving the thermoresponsive changes individually in direct luminous and hemispheric solar transmittance, both of which are crucial properties of energy-saving smart windows. The features of a 1280 × 1920–pixel color POM image were extracted by the latest pretrained algorithm, EfficientNet-B7, as a 2560-dimensional vector and then reduced into a two-dimensional space for clustering and visualization using the uniform manifold approximation and projection (UMAP) algorithm while efficiently preserving the global structures of the distance relationship in a high-dimensional space. The feature vectors in the UMAP space were correlated with the thermoresponsive transmittance and classified using hierarchical clustering analysis. The extracted features belonging to some clusters were also correlated with the fabrication parameters. The PNLCs here were produced from various raw materials under different fabrication conditions. These analyses and predictability are extensively applied to different PNLCs for stimuli-responsive optical devices, such as solar- and privacy-control windows.