Modeling the Mechanical Properties of a Polymer-Based Mixed-Matrix Membrane Using Deep Learning Neural Networks

Abstract

Polylactic acid (PLA), the second most produced biopolymer, was selected for the fabrication of mixed-matrix membranes (MMMs) via the incorporation of HKUST-1 metal–organic framework (MOF) particles into a PLA matrix with the aim of improving mechanical characteristics. A deep learning neural network (DLNN) model was developed on the TensorFlow 2 backend to predict the mechanical properties, stress, strain, elastic modulus, and toughness of the PLA/HKUST-1 MMMs with different input parameters, such as PLA wt%, HKUST-1 wt%, casting thickness, and immersion time. The model was trained and validated with 1214 interpolated datasets in stratified fivefold cross validation. Dropout and early stopping regularizations were applied to prevent model overfitting in the training phase. The model performed consistently for the unknown interpolated datasets and 26 original experimental datasets, with coefficients of determination (R2) of 0.93–0.97 and 0.78–0.88, respectively. The results suggest that the proposed method can build effective DLNNmodels using a small dataset to predict material properties.