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Abstract <p>Electrospinning offers exceptional flexibility in fabricating nanofibers, yet achieving precise control over fiber diameter, which is crucial for tailoring material properties, remains a challenge due to the complex and nonlinear interactions among various process parameters. This study presents a literature-derived, multi-material predictive-optimization strategy for the computational control of electrospun fiber diameter. A dataset containing 3000 data points was collected from published scientific research articles on electrospinning, covering a wide range of polymers, solvents, and process parameters, and was used to develop predictive machine learning (ML) models for fiber diameter prediction. Among the developed models, the extreme gradient boosting (XGB) model performed best, achieving a coefficient of determination (<i>R</i><sup>2</sup>) value of 0.94 and delivering low prediction errors (root mean square error [RMSE]: 275.02&#xa0;nm, mean absolute error [MAE]: 75.40&#xa0;nm) on the unseen data. Experimental validation using polystyrene (PS) and polyvinyl chloride (PVC) nanofibers showed close alignment between predicted and measured fiber diameters, supporting the practical reliability of the predictive model under real electrospinning conditions. To move beyond forward prediction, the trained XGB model was integrated with a genetic algorithm (GA) to enable surrogate-based inverse design of electrospinning parameters for user-defined target fiber diameters. The GA identified parameter combinations corresponding to target diameters between 100&#xa0;nm and 4000&#xa0;nm with low surrogate-model fitness errors. For target diameters of 400&#xa0;nm and 600&#xa0;nm, the GA achieved fitness errors of 0.03&#xa0;nm and 0.35&#xa0;nm, respectively. Furthermore, the integrated XGB-GA approach yielded a perfect linear correlation (<i>R</i><sup>2</sup> = 1.00) between the target fiber diameters and the predicted fiber diameters within the GA-optimized subset, demonstrating the effectiveness of the optimization strategy for precise diameter control across various electrospinning conditions. Overall, this study provides a data-driven approach for predicting fiber diameter and guiding electrospinning parameter selection while reducing dependence on trial-and-error experimentation within the material systems represented in the dataset.</p> Graphical abstract <p></p>

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Computational precision control of electrospun polymer nanofiber morphology using machine learning and genetic algorithm optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Balakrishnan Subeshan,
  • Eylem Asmatulu

摘要

Abstract

Electrospinning offers exceptional flexibility in fabricating nanofibers, yet achieving precise control over fiber diameter, which is crucial for tailoring material properties, remains a challenge due to the complex and nonlinear interactions among various process parameters. This study presents a literature-derived, multi-material predictive-optimization strategy for the computational control of electrospun fiber diameter. A dataset containing 3000 data points was collected from published scientific research articles on electrospinning, covering a wide range of polymers, solvents, and process parameters, and was used to develop predictive machine learning (ML) models for fiber diameter prediction. Among the developed models, the extreme gradient boosting (XGB) model performed best, achieving a coefficient of determination (R2) value of 0.94 and delivering low prediction errors (root mean square error [RMSE]: 275.02 nm, mean absolute error [MAE]: 75.40 nm) on the unseen data. Experimental validation using polystyrene (PS) and polyvinyl chloride (PVC) nanofibers showed close alignment between predicted and measured fiber diameters, supporting the practical reliability of the predictive model under real electrospinning conditions. To move beyond forward prediction, the trained XGB model was integrated with a genetic algorithm (GA) to enable surrogate-based inverse design of electrospinning parameters for user-defined target fiber diameters. The GA identified parameter combinations corresponding to target diameters between 100 nm and 4000 nm with low surrogate-model fitness errors. For target diameters of 400 nm and 600 nm, the GA achieved fitness errors of 0.03 nm and 0.35 nm, respectively. Furthermore, the integrated XGB-GA approach yielded a perfect linear correlation (R2 = 1.00) between the target fiber diameters and the predicted fiber diameters within the GA-optimized subset, demonstrating the effectiveness of the optimization strategy for precise diameter control across various electrospinning conditions. Overall, this study provides a data-driven approach for predicting fiber diameter and guiding electrospinning parameter selection while reducing dependence on trial-and-error experimentation within the material systems represented in the dataset.

Graphical abstract