Prediction of bead formation in PVDF fiber across different solvent systems using Interpretable Machine Learning
Year: 2024
Type: article
Abstract: Bead formation is a typical ramification of electrospun fibers during electrospinning. Presence or absence of beads controls the fiber properties, which dictates its usefulness in diverse applications. However, bead formation is a complex non-linear process influenced by solution properties as well as electrospinning process parameters, mostly explored through trial-and-error experiments. Thus, being able to predict bead formation and identify its causal properties could have tremendous techno-economic value by reducing cost of experimentation. This is challenging as these structure-property relationships between experimental features and bead formation are inherently complex and modelling them requires large datasets from diverse experiments with multiple solvents, which are not commonly available. Here, we developed our own Electrospun Fiber Experimental Attributes Dataset (FEAD) dataset, a curated meta-database of experimental data available in literature, supplemented with our own experiments. Combining it with multiple machine learning models, we showed that while an increase in polymer concentration and applied voltage leads to fewer beads, a higher Flory-Huggins parameter supports increased bead formation. Further, we adopted a game theory-based model agnostic interpretation technique called SHAP (SHapley Additive exPlanations) to identify features contributing towards the occurrence of beads and their relative significance. This novel framework successfully predicted bead formation across various PVDF-polymer solvent systems and demonstrates how community meta datasets, cutting-edge machine learning techniques, and model interpretability methods could be seamlessly integrated to reduce the number of experiments required for developing high quality PVDF fibers. • Introduces a novel framework for PVDF fibers using an enhanced FEAD dataset and explainable ML for structure-property links. • XGBoost is optimal, with SHAP revealing insights like fewer beads at higher polymer concentrations in PVDF fibers. • Framework generalizes relationships, minimizing costly lab tests for developing PVDF fibers with desired morphologies. • Encourages community contributions to the FEAD dataset on Zenodo, fostering adaptable predictive models for experiments.
Source: Polymer
Institution: Technische Universität Braunschweig
Language: English
Subfield: Biomaterials
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,860
Awards: G4339003044, G4434139671
OpenAlex ID: W4405747122