Approaches for the fabrication of biomaterials are currently numerous, with a wide diversity of available material precursors, chemistries, and processing technologies. Owing to the complex nature of the human body, biomaterials are targeted for applications with highly diverse performance demands. Traditional strategies based on trial and error have fallen short of predicting the ideal parameters required to produce adequate structures to meet these challenges. Although the design of experiments enables reducing experimental testing, it has failed to predict complex, multi-factorial processing effects, including experimental variability. Despite being often overlooked, experimental variability is an important aspect in biomaterials, which are often processed from source materials with significant compositional variability (e.g., natural polymers), along with processing methodologies frequently undertaken under poorly controlled environmental conditions. Here, a machine learning approach based on Gaussian processes (GPs) is developed to identify patterns and correlations between fabrication conditions and material properties. Flexible soft membrane-based tubular materials obtained by polyelectrolyte complexation are used as a model biomaterial characterized by multi-parametric design inputs. Using GPs, the effects of processing parameters on the magnitude and variability of key properties like permeability, porosity, thickness, opacity, and swelling ratio are quantified. This approach is expected to enable more reliable and predictable biomaterial fabrication.
Alexaki et al. (2026) studied this question.