Advanced Data-Driven Methodology Integrating Predictive Machine Learning Models with Evolutionary Algorithm Optimization for Accurate Prediction and Control of Electrospun Polymer Nanofiber Fabrication

Electrospinning is a widely used nanofabrication technique capable of producing fibers with a range of diameters, morphologies, and porosities through the adjustment of experimental parameters. However, achieving reliable fiber diameter tuning remains challenging because of the complex, nonlinear interdependence among multiple electrospinning variables. In this study, a data-driven methodology is proposed that integrates predictive machine learning (ML) modeling with evolutionary algorithm-based optimization, specifically employing a genetic algorithm (GA), to predict fiber diameter and guide electrospinning parameter selection across nano- and microscale ranges. A curated dataset comprising 388 data points from 30 scientific publications was developed, focusing exclusively on polyacrylonitrile (PAN) dissolved in dimethylformamide (DMF). Multiple ML models were trained and tested to predict fiber diameter as a function of key electrospinning parameters. Among the evaluated ML models, the eXtreme gradient boosting (XGB) model achieved the highest predictive performance, yielding a coefficient of determination (R2) value of 0.93 with low prediction errors (root mean square error [RMSE]: 127.76 nm, mean absolute error [MAE]: 56.27 nm) on the test set. Experimental validation was performed by fabricating electrospun PAN nanofibers under one independent set of conditions, with scanning electron microscopy (SEM) showing close agreement between predicted and actual fiber diameters. The trained XGB model was subsequently integrated with a GA to identify electrospinning parameter sets for user-defined target fiber diameters ranging from 100 to 2000 nm. The evolutionary optimization process exhibited rapid convergence with low fitness error when evaluated using the trained predictive model. Overall, this study demonstrates the potential of a data-driven methodology to generate model-guided candidate conditions for target-driven PAN-DMF electrospinning, subject to broader experimental validation.