Pore-permeability evolution in fuel-related copolymers via machine learning

This study establishes a machine learning-enhanced computational framework to decode the pore-permeability constitutive relationship in fuel-related P(AA₀.₃-r-St₀.₇)₁₈₅ random copolymers. This copolymer exhibits good oil/fast resistance and controllable nanopore structure, making it ideal for fuel separation membranes, fuel filtration, and gas reservoir permeation in fuel-related energy systems. Through multi-scale modeling combining (i) Materials Studio-based conformational sampling with Markov chain Monte Carlo optimization, and (ii) CNN-assisted grayscale analysis of simulated pore structure images, we quantitatively correlate nanoscale porosity features (Connolly surface area, structural porosity from image processing, and specific surface area) with macroscopic transport properties. A simple CNN is used as an auxiliary image-processing tool to efficiently extract porosity from grayscale images. The Kozeny-Carman equation reveals a bifurcated linear correlation, exhibiting an inflection point at a porosity of 0.131, which corresponds to 23.2% cell-face porosity in the optimized models. Microstructural analysis indicates this critical transition stems from pore network reorganization, where permeability evolution shifts from rapid growth (< 0.131) to gradual increase (> 0.131). Validated against experimental trends, this hybrid computational approach demonstrates superior robustness to empirical methods by simultaneously capturing (a) molecular-level conformational dynamics through MS simulations, and (b) mesoscale porosity-permeability coupling via image-based machine learning. The framework provides a predictive tool for designing functional copolymer membranes for fuel processing and energy storage applications, with potential extensions to other porous material systems through transfer learning of the established microstructure-transport.