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Understanding Fabrication Variability in Core-Shell Soft Biomaterials Using Stochastic Artificial Intelligence.

Maria Alexaki¹, Lília M S Dias^2,3,4, Raquel C Gonçalves¹

¹CICECO-Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Aveiro, Portugal.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|April 1, 2026

Summary

This summary is machine-generated.

Machine learning using Gaussian processes (GPs) predicts biomaterial properties by analyzing fabrication conditions. This approach enhances the reliability and predictability of creating advanced biomaterials for diverse applications.

Keywords:

Gaussian processes hydrogels soft biomaterials stochastic machine learning uncertainty

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Area of Science:

Biomaterials Science
Materials Engineering
Machine Learning Applications

Background:

Biomaterial fabrication involves diverse precursors, chemistries, and technologies to meet complex biological demands.
Traditional trial-and-error and design of experiments methods struggle to predict multi-factorial processing effects and experimental variability.
Variability in natural polymer precursors and uncontrolled processing conditions complicate biomaterial development.

Purpose of the Study:

To develop a machine learning approach for identifying correlations between biomaterial fabrication conditions and properties.
To quantify the effects of processing parameters on the magnitude and variability of key biomaterial characteristics.
To enable more reliable and predictable biomaterial fabrication.

Main Methods:

A machine learning approach utilizing Gaussian processes (GPs) was developed.
Flexible soft membrane-based tubular materials fabricated via polyelectrolyte complexation served as a model system.
GPs were employed to analyze multi-parametric design inputs and quantify effects on material properties and their variability.

Main Results:

Gaussian processes successfully identified patterns and correlations between fabrication conditions and material properties.
The effects of processing parameters on permeability, porosity, thickness, opacity, and swelling ratio were quantified.
Both the magnitude and variability of these key properties were effectively modeled.

Conclusions:

Gaussian processes offer a powerful tool for understanding and predicting biomaterial properties.
This machine learning approach can overcome limitations of traditional methods in handling complex processing effects and variability.
The developed methodology promises to enhance the reliability and predictability of biomaterial fabrication for advanced applications.