A surrogate-based inverse design framework for targeted diameter control of electrospun nanofibers

Electrospinning is a high-throughput technique for producing nanofibers. The diameter of such nanofibers governs key properties such as surface area, porosity, and mechanical strength. Precise diameter control is therefore crucial for applications from filtration to tissue engineering, yet optimizing processing conditions for targeted diameter fabrication typically relies on slow, costly trial-and-error experiments. This study presents a data-driven inverse-design framework that replaces traditional trial-and-error optimization with predictive modeling to achieve precise diameter control. Eleven regression models were evaluated on a dataset of 96 poly(vinyl alcohol) (PVA) experiments, with Extreme Gradient Boosting (XGBoost) emerging as the best surrogate (test [Formula: see text]). SHAP analysis confirmed applied voltage and solution concentration as the most influential parameters, consistent with physical principles. In the optimization stage, Particle Swarm Optimization (PSO) achieved the highest inverse design accuracy ([Formula: see text], MAE [Formula: see text]). This framework enables rapid, efficient design of nanofibers with specified properties and is readily adaptable to other materials and fabrication processes.